An Improved Framework for Classification of Skin Cancer Lesions using Transfer Learning

Pigmented skin lesions are common spots or growths on the skin that originate from melanocyte cells. Skin cancer occurs as a result of the uncontrolled division of melanocyte cells. Skin cancer is common worldwide and its incidence has been increasing. Timely and accurate diagnosis of skin cancer is important in reducing deaths. Skin cancer cases are diagnosed by expert dermatologists, but the number of experts is insufficient for the population. Dermoscopy is one of the most commonly used methods for imaging skin lesions. Sometimes misdiagnosis can occur in the interpretation of dermoscopic images by human experts. Computer-aided systems help to make accurate and objective decisions in the diagnosis of skin lesions. Convolutional neural network(CNN) is a deep learning technique commonly used in the field of computer vision and is also widely used in medical image analysis In this study, a CNN model was proposed to classify seven different skin lesions in the HAM10000 dataset. The model provided a classification accuracy of 91.51%. The performance of the model was compared with similar studies in the literature and it was found that it showed higher success than most studies. The model was connected to a web application and evaluated in two phases by seven expert dermatologists. In the first phase, it was concluded that the model could diagnose skin lesions with 90.28% accuracy in practice. In the second phase, the model corrected the experts' misdiagnoses by 11.14%.

Skin Cancer is an extensive and possibly dangerous disorder that requires early detection for effective treatment. Add specific global statistics on skin cancer prevalence and mortality to emphasize the importance of early detection. Example: “Skin cancer accounts for 1 in 5 diagnosed cancers globally, with melanoma causing over 60,000 deaths annually. Manual skin cancer screening is both time-intensive and expensive. Deep learning (DL) techniques have shown exceptional performance in various applications and have been applied to systematize skin cancer diagnosis. However, training DL models for skin cancer diagnosis is challenging due to limited available data and the risk of overfitting. Traditionally approaches have High computational costs, a lack of interpretability, deal with numerous hyperparameters and spatial variation have always been problems with machine learning (ML) and DL. An innovative method called adaptive learning has been developed to overcome these problems. In this research, we advise an intelligent computer-aided system for automatic skin cancer diagnosis using a two-stage transfer learning approach and Pre-trained Convolutional Neural Networks (CNNs). CNNs are well-suited for learning hierarchical features from images. Annotated skin cancer photographs are utilized to detect ROIs and reset the initial layer of the pre-trained CNN. The lower-level layers learn about the characteristics and patterns of lesions and unaffected areas by fine-tuning the model. To capture high-level, global features specific to skin cancer, we replace the fully connected (FC) layers, responsible for encoding such features, with a new FC layer based on principal component analysis (PCA). This unsupervised technique enables the mining of discriminative features from the skin cancer images, effectively mitigating overfitting concerns and letting the model adjust structural features of skin cancer images, facilitating effective detection of skin cancer features. The system shows great potential in facilitating the initial screening of skin cancer patients, empowering healthcare professionals to make timely decisions regarding patient referrals to dermatologists or specialists for further diagnosis and appropriate treatment. Our advanced adaptive fine-tuned CNN approach for automatic skin cancer diagnosis offers a valuable tool for efficient and accurate early detection. By leveraging DL and transfer learning techniques, the system has the possible to transform skin cancer diagnosis and improve patient outcomes.

People around the world suffer from a wide range of illnesses. The most dangerous of them all is cancer. Of all the cancers, skin cancer is the one that is spreading the fastest. The reason for this is because skin cells grow abnormally. Skin cancer is spreading around the world in part due to an increase in UV light on Earth's surface. The two most prevalent kinds of skin cancer are benign and malignant. Despite undergoing costly and time-consuming therapies, skin cancer mortality rates do not decrease. Early identification of skin cancer in its early stages is useful in lowering the death rate. Deep learning is being utilized in the modern world to identify illnesses. Convolutional neural networks (CNNs) improve the accuracy of image classification in the detection of skin cancer. This study compares the operational procedures of several CNN models in order to identify which produces the best outcomes. CNN models are analyzed using pre-trained models such as VGG16, Res-Net50, and Inception Resnet V2. Due to differences in their layer numbers, these models operate in distinct ways. Certain models function better than others, depending on their layers and how they operate. A Kaggle picture dataset containing both benign and cancerous data has been extracted. There are 3287 photos of benign and malignant skin cancer in this dataset. We have obtained accurate results for VGG16 (85%), ResNet50 (69%), and Inception Resnet V2 (83%), using various techniques. This study compares these results according to the model's operation. We compare precision, working process, and model layer numbers.

Skin cancer is a kind of cancer that is usually diagnosed by images from dermoscopy. In recent years, researchers have attempted to utilize deep learning technology, especially convolution neural networks (CNN), in the recognition of skin cancer images. Many CNN models have already performed great applicability, like DenseNet, Inception, and Xception. This paper carried a comparative experiment on ISIC 2019 challenge dataset which includes 25,331 skin cancer images of 8 different kinds. On the classification task on the ISIC 2019, it introduced 6 models, VGGNet19, ResNet50, ResNet152, DenseNet201, Inception-v3, and Xception, then conducted a comparative analysis of their performance involving 2 methods (data enhance and transfer learning) and 2 optimizers (Adam and SGD), aiming to explore the impact of different methods and structures on the accuracy, in order to find traits for potential models of higher accuracy. In the 24 groups of results, Xception with data enhance, transfer learning (pretraining) and Adam optimizer had the highest accuracy of 83.8%, while VGGNet19 without transfer learning had the lowest of 66.67%. The influence of transfer leaning is positive on all models, both on accuracy and training time; similar to Adam optimizer, except for a noticeable enhancement effect on Inception-v3 and Xception. The data enhance method applied in this paper had a weak, non-directed impact. Possible reasons for this phenomenon are discussed in depth in the study.

Skin cancer ranks among the most commonly diagnosed forms of cancer worldwide, with millions of new cases identified annually. Over 2 million cases of non-melanoma skin cancer and approximately 132,000 melanoma cases are reported each year . The study aims is to develop an efficient deep learning model using CNN’s architectures to accurately classify skin lesions as malignant or benign based on dermoscopic images. This study explored the use of Convolutional Neural Network (CNN) models using AlexNet and ResNet architectures for skin cancer classification. The performance of both models were evaluated based on key metrics such as accuracy, precision, recall, and F1-score. AlexNet was trained on 1,200 skin lesion images and validated on 600 images,training spanned 10 epochs. ResNet was similarly trained and validated after 10 epochs. Both architectures were compared under identical conditions (same dataset, pre-processing, optimizer, and hyperparameters). ResNet outperformed AlexNet in all performance metrics of accuracy as 91.00% and 59.00% respectively. This research explored the performance of CNN(ResNet50) and CNN(AlexNet). This validates that deeper architectures with residual connections, batch normalization, and optimized feature extraction are more effective for binary skin cancer classification.

Among the many deep learning methods, the convolutional neural network (CNN) model has an excellent performance in image recognition. Research on identifying and classifying image datasets using CNN is ongoing. Animal species recognition and classification with CNN is expected to be helpful for various applications. However, sophisticated feature recognition is essential to classify quasi-species with similar features, such as the quasi-species of parrots that have a high color similarity. The purpose of this study is to develop a vision-based mobile application to classify endangered parrot species using an advanced CNN model based on transfer learning (some parrots have quite similar colors and shapes). We acquired the images in two ways: collecting them directly from the Seoul Grand Park Zoo and crawling them using the Google search. Subsequently, we have built advanced CNN models with transfer learning and trained them using the data. Next, we converted one of the fully trained models into a file for execution on mobile devices and created the Android package files. The accuracy was measured for each of the eight CNN models. The overall accuracy for the camera of the mobile device was 94.125%. For certain species, the accuracy of recognition was 100%, with the required time of only 455 ms. Our approach helps to recognize the species in real time using the camera of the mobile device. Applications will be helpful for the prevention of smuggling of endangered species in the customs clearance area.

Accurate skin cancer classification is essential for early diagnosis and effective treatment planning, enabling timely interventions and improved patient outcomes. In this paper, the performance of four pre-trained models—two convolutional neural networks (ResNet50 and VGG19) and two vision transformers (ViT-b16 and ViT-b32)—is evaluated in distinguishing malignant from benign skin cancers using a publicly available dermoscopic dataset. Among these models, ResNet50 achieved the highest performance across all the evaluation metrics, with accuracy, precision, and recall of 89.09% and an F1 score of 89.08%, demonstrating its ability to effectively capture complex patterns in skin lesion images. While the other models produced competitive results, ResNet50 exhibited superior robustness and consistency. To enhance model interpretability, two eXplainable Artificial Intelligence (XAI) techniques, Local Interpretable Model-Agnostic Explanations (LIME) and integrated gradients, were employed to provide insights into the decision-making process, fostering trust in automated diagnostic systems. These findings underscore the potential of deep learning for automated skin cancer classification and highlight the importance of model transparency for clinical adoption. As AI technology continues to evolve, its integration into clinical workflows could improve diagnostic accuracy, reduce the workload of healthcare professionals, and enhance patient outcomes.

Skin cancers are one of the most common cancers in the world. Early detections and treatments of skin cancers can greatly improve the survival rates of patients. In this paper, a skin lesions classification system is developed with deep convolutional neural networks of ResNet50, which may help dermatologists to recognize skin cancers earlier. We utilize the ResNet50 as a pre-trained model. Then, by transfer learning, it is trained on our skin lesions dataset. Image preprocessing and dataset balancing methods are used to increase the accuracy of the classification model. In classification of skin diseases, our model achieves an overall accuracy of 83.74% on nine-class skin lesions. The experimental results show an impressive effect of the ResNet50 model in finegrained skin lesions classification and skin cancers recognition.

Skin cancer is one of the most dangerous types of cancer due to its immediate appearance and the possibility of rapid spread. It arises from uncontrollably growing cells, rapidly dividing cells in one area of the body, invading other bodily tissues, and spreading throughout the body. Early detection helps prevent cancer progress from reaching critical levels, reducing the risk of complications and the need for more aggressive treatment options. Convolutional neural networks (CNNs) revolutionize skin cancer diagnosis by extracting intricate features from images, enabling an accurate classification of lesions. Their role extends to early detection, providing a powerful tool for dermatologists to identify abnormalities in their nascent stages, ultimately improving patient outcomes. This study proposes a novel deep convolutional neural network (DCNN) approach to classifying skin cancer lesions. The proposed DCNN model is evaluated using two unbalanced datasets, namely HAM10000 and ISIC-2019. The DCNN model is compared with other transfer learning models, including VGG16, VGG19, DenseNet121, DenseNet201, and MobileNetV2. Its performance is assessed using four widely used evaluation metrics: accuracy, recall, precision, F1-score, specificity, and AUC. The experimental results demonstrate that the proposed DCNN model outperforms other deep learning (DL) models that utilized these datasets. The proposed DCNN model achieved the highest accuracy with the HAM10000 and ISIC-2019 datasets, reaching 98.5%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$98.5\\%$$\\end{document} and 97.1%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$97.1\\%$$\\end{document}, respectively. These experimental results show how competitive and successful the DCNN model is in overcoming the problems caused by class imbalance and raising skin cancer classification accuracy. Furthermore, the proposed model demonstrates superior performance, particularly excelling in terms of accuracy, compared to other recent studies that utilize the same datasets, which highlights the robustness and effectiveness of the proposed DCNN.

Skin cancer diagnosis challenges dermatologists due to its complex visual variations across diagnostic categories. Convolutional neural networks (CNNs), specifically the Efficient Net B0-B7 series, have shown superiority in multiclass skin cancer classification. This study addresses the limitations of visual examination by presenting a tailored preprocessing pipeline designed for Efficient Net models. Leveraging transfer learning with pre-trained ImageNet weights, the research aims to enhance diagnostic accuracy in an imbalanced multiclass classification context. The study develops a specialized image preprocessing pipeline involving image scaling, dataset augmentation, and artifact removal tailored to the nuances of Efficient Net models. Using the Efficient Net B0-B7 dataset, transfer learning fine-tunes CNNs with pre-trained ImageNet weights. Rigorous evaluation employs key metrics like Precision, Recall, Accuracy, F1 Score, and Confusion Matrices to assess the impact of transfer learning and fine-tuning on each Efficient Net variant's performance in classifying diverse skin cancer categories. The research showcases the effectiveness of the tailored preprocessing pipeline for Efficient Net models. Transfer learning and fine-tuning significantly enhance the models' ability to discern diverse skin cancer categories. The evaluation of eight Efficient Net models (B0-B7) for skin cancer classification reveals distinct performance patterns across various cancer classes. While the majority class, Benign Kertosis, achieves high accuracy (>87%), challenges arise in accurately classifying Eczema classes. Melanoma, despite its minority representation (2.42% of images), attains an average accuracy of 80.51% across all models. However, suboptimal performance is observed in predicting warts molluscum (90.7%) and psoriasis (84.2%) instances, highlighting the need for targeted improvements in accurately identifying specific skin cancer types. The study on skin cancer classification utilizes EfficientNets B0-B7 with transfer learning from ImageNet weights. The pinnacle performance is observed with EfficientNet-B7, achieving a groundbreaking top-1 accuracy of 84.4% and top-5 accuracy of 97.1%. Remarkably efficient, it is 8.4 times smaller than the leading CNN. Detailed per-class classification exactitudes through Confusion Matrices affirm its proficiency, signaling the potential of EfficientNets for precise dermatological image analysis.

This study investigates the classification of skin cancer lesions utilizing Convolutional Neural Networks (CNNs) on the HAM10000 dataset. The collection includes photos of seven unique categories of skin lesions: melanocytic nevi, melanoma, benign keratosis-like lesions, basal cell carcinoma, actinic keratoses, vascular lesions, and dermatofibroma. Our main goals were to examine diagnostics efficacy over several lesion types and assess CNN performance in precisely categorizing dermatoscopic pictures. Label encoding and data balancing were used to ensure class representation. For regularization, we built sequential CNN models with convolutional, pooling, and dense layers and dropout and batch normalization. Transfer learning and Adam optimization were used in training. Our classification accuracy averages above 73.8%, demonstrating solid performance across lesion types. We tested the models' generalization ability on new data and found consistent performance. Confusion matrix analysis showed accurate categorization with low misclassification. This research improves early identification and intervention, improving patient outcomes and healthcare efficiency.

Deep convolutional neural networks (CNNs) enable learning trainable, highly representative and hierarchical image feature from sufficient training data which makes rapid progress in computer vision possible. There are currently three major techniques that successfully employ CNNs to medical image classification: training the CNN from scratch, using off-the-shelf pretrained CNN features, and transfer learning , i.e., fine-tuning CNN models pretrained from natural image dataset (such as large-scale annotated natural image database: ImageNet) to medical image tasks. In this chapter, we exploit three important factors of employing deep convolutional neural networks to computer-aided detection problems. First, we exploit and evaluate several different CNN architectures including from shallower to deeper CNNs: classical CifarNet, to recent AlexNet and state-of-the-art GoogLeNet and their variants. The studied models contain five thousand to 160 million parameters and vary in the numbers of layers. Second, we explore the influence of dataset scales and spatial image context configurations on medical image classification performance. Third, when and why transfer learning from the pretrained ImageNet CNN models (via fine-tuning) can be useful for medical imaging tasks are carefully examined. We study two specific computer-aided detection (CADe) problems, namely thoracoabdominal lymph node (LN) detection and interstitial lung disease (ILD) classification. We achieve the state-of-the-art performance on the mediastinal LN detection and report the first fivefold cross-validation classification results on predicting axial CT slices with ILD categories. Our extensive quantitative evaluation, CNN model analysis, and empirical insights can be helpful to the design of high-performance CAD systems for other medical imaging tasks, without loss of generality.

In the realm of computer vision, image classification is a critical issue with many applications, including multimedia content analysis, security and surveillance, and medical imaging. The accuracy of image classification algorithms has considerably increased with the development of deep learning. The discipline of image classification has dramatically benefited from the usage of pre-trained Convolutional Neural Network (CNN) models. In this study, we perform image classification on the Wang dataset composed of 1000 images separated into ten categories, using six different pre-trained CNN models. The research made use of pre-trained versions of VGG16, Densenet, Mobilenet, Inception V3, Resnet50, and Xception models. We assessed the model performances in terms of training and testing accuracy. Testing accuracies for ten unique batches of data were calculated and averaged out. Densenet outperformed state-of-the-art models like Xception by a small margin, mainly due to low data availability. The findings of this study show how pre-trained models have advanced the field of image classification and shed light on how well these models perform in diverse image classification tasks. This study can serve as a valuable reference for researchers and practitioners in the field of computer vision and deep learning and help inform their choices when selecting pre-trained models for image classification, depending on their needs, while also considering data availability and computational constraints.

It is well known that chronic exposure to ultraviolet (UV) radiation present in sunlight is responsible for the induction of most nonmelanoma skin cancer (NMSC) in humans. Wavelengths in the UV-B (290–320 nm) region of the solar spectrum are absorbed into the skin, producing erythema, burns, and eventually skin cancer. NMSC is the most common type of human cancer. Recent surveys indicate that around one million new cases of skin cancer are diagnosed each year in the United States, about 70% of which result from repeated exposure of the skin to sunlight. Laboratory studies have shown that UV-B region of the solar spectrum is responsible for this effect. The first step in UV skin carcinogenesis involves the induction of DNA damage. Occasional mistakes during the repair of this damage leads to the incorporation of wrong bases into the genetic material. The DNA damage that is left unrepaired may also disrupt cellular processes by obstructing the DNA and RNA synthesizing machineries and introduce wrong bases into the DNA. These types of mistakes often result in mutation leading to loss or inappropriate expression of affected genes. Recent studies indicate that genetic alterations in the p53 tumor suppressor gene play an important role in the development of skin cancer. The p53 protein is also involved in programmed cell death (apoptosis), and it has been proposed that p53 serves as a “guardian of the genome” by aiding DNA repair or causing elimination of cells with excessive DNA damage. Unrepaired photoproducts in the p53 gene are transformed into mutations thereby initiating the process of carcinogenesis. Following repeated exposures to UV, keratinocytes carrying p53 mutations acquire a growth advantage by virtue of their increased resistance to apoptosis. Recent studies have shown that UV-B damaged keratinocytes (sunburn cells) are eliminated by Fas/Fas-ligand interaction and that this pathway is dysregulated during UV skin carcinogenesis resulting in accumulation of p53 mutations in DNA damaged keratinocytes. These results demonstrate a link between p53 pathway and Fas/Fas-ligand pathway and that dysregulation of both pathways can lead to the pathogenesis of UV-induced skin cancer. Several studies have shown that UV induces unique types of p53 mutations in skin cancers at a high frequency that are not commonly found in other types of human cancer. Analogous to human skin cancers, skin cancers induced in laboratory mice by UV radiation also display UV signature p53 mutations at a high frequency. More interestingly, p53 mutations are also present in sun-exposed skin and it can serve as an indicator of prior solar exposure in humans. It has been shown that p53 mutations in mouse skin arise as early as one week of chronic UV-irradiation and the frequency of p53 mutations reach a maximum at 4–8 week of UV exposure. These results suggest that p53 mutations arise well before skin cancer development and that they can serve as a surrogate early biological endpoint in skin cancer prevention studies. In fact, it has been shown that application of SPF-15 sunscreens to mouse skin before each UV-irradiation protect mice against induction of p53 mutations as well as skin cancer development. In addition to the p53 gene, another tumor suppressor gene known as patched (ptc) has been implicated in nevoid basal cell carcinoma syndrome (NBCCS). NBCCS, also called basal cell nevus syndrome or Gorlin's syndrome, is a rare autosomal dominant disorder characterized by multiple BCCs that appear at a young age on sun-exposed areas of the skin. Studies of NBCCS patients have shown that they have both germ-line and somatic mutations in the ptc gene. Somatic ptc mutations have been found in BCCs from otherwise normal individuals suggesting that genetic alterations in the ptc gene may also play a role in the development of BCC. In summary, recent advances have aided in the understanding of the mechanisms by which UV radiation induces skin cancer. Continued efforts should result in a greater understanding of the genetic mechanisms involved in the function of tumor suppressor and oncogenes that are now known, as well as genes yet to be discovered. The efforts of research in skin cancer may help to increase overall awareness of the harmful effects of UV exposure and result in better methods of skin cancer prevention and treatment.

The chapter delves into the transformative impact of Convolutional Neural Networks (CNNs) on medical imaging, highlighting their ability to enhance diagnostic accuracy, streamline workflows, and enable real-time image analysis. It provides a comprehensive overview of CNN architectures, their principles, and their integration into diverse medical imaging modalities, such as X-rays, MRIs, and CT scans. Through case studies, the chapter explores CNN applications in detecting brain tumours, pneumonia, and skin cancer, while addressing advanced techniques like transfer learning, GAN-based data augmentation, and hybrid models. The discussion emphasizes the role of Explainable AI (XAI) in overcoming challenges related to model interpretability and ethical considerations, such as patient confidentiality and bias. With a focus on future advancements, including personalized medicine and seamless integration into clinical workflows, the chapter underscores CNNs' potential to redefine diagnostic imaging, improving patient outcomes and revolutionizing healthcare.