A deep convolutional neural network-based pigmented skin lesion classification application and experts evaluation

A new approach for classification skin lesion based on transfer learning, deep learning, and IoT system

Skin cancer has emerged as a significant global health concern, with a growing incidences worldwide. Timely detection of skin cancer is pivotal, as early diagnosis greatly enhances the prospects of successful treatment. While skin cancer is commonly associated with sun-exposed areas, it can also manifest in less typical regions. Alarmingly, over 123,000 melanoma and 30,00,000 non-melanoma cases are reported annually across the globe. Given the rapid increase in skin cancer cases, early identification of both cancerous and non-cancerous lesions is imperative. In response to the limitations faced by dermatologists in providing consistently accurate diagnoses, there is a pressing need for an automated and efficient diagnostic system. This study presents an innovative and automated approach for skin cancer classification, achieving superior evaluation metrics and enhanced accuracy compared to prior studies and expert dermatologists. Our proposed Convolutional Neural Network (CNN) model, augmented by leveraging pre-trained models, attains an accuracy rate of 94%, with the highest among the pre-trained models reaching 97%. This research offers a promising avenue to revolutionize skin cancer diagnosis, facilitating early intervention and improved patient outcomes.

In recent years, computerized biomedical imaging and analysis have become extremely promising, more interesting, and highly beneficial. They provide remarkable information in the diagnoses of skin lesions. There have been developments in modern diagnostic systems that can help detect melanoma in its early stages to save the lives of many people. There is also a significant growth in the design of computer-aided diagnosis (CAD) systems using advanced artificial intelligence. The purpose of the present research is to develop a system to diagnose skin cancer, one that will lead to a high level of detection of the skin cancer. The proposed system was developed using deep learning and traditional artificial intelligence machine learning algorithms. The dermoscopy images were collected from the PH2 and ISIC 2018 in order to examine the diagnose system. The developed system is divided into feature-based and deep leaning. The feature-based system was developed based on feature-extracting methods. In order to segment the lesion from dermoscopy images, the active contour method was proposed. These skin lesions were processed using hybrid feature extractions, namely, the Local Binary Pattern (LBP) and Gray Level Co-occurrence Matrix (GLCM) methods to extract the texture features. The obtained features were then processed using the artificial neural network (ANNs) algorithm. In the second system, the convolutional neural network (CNNs) algorithm was applied for the efficient classification of skin diseases; the CNNs were pretrained using large AlexNet and ResNet50 transfer learning models. The experimental results show that the proposed method outperformed the state-of-art methods for HP2 and ISIC 2018 datasets. Standard evaluation metrics like accuracy, specificity, sensitivity, precision, recall, and F-score were employed to evaluate the results of the two proposed systems. The ANN model achieved the highest accuracy for PH2 (97.50%) and ISIC 2018 (98.35%) compared with the CNN model. The evaluation and comparison, proposed systems for classification and detection of melanoma are presented.

Skin cancer is a class of disorder defined by the growth of abnormal cells on the body. Accurately identifying and diagnosing skin lesions is quite difficult because skin malignancies share many common characteristics and a wide range of morphologies. To face this challenge, deep learning algorithms have been proposed. Deep learning algorithms have shown diagnostic efficacy comparable to dermatologists in the discipline of images-based skin lesion diagnosis in recent research articles. This work proposes a novel deep learning algorithm to detect skin cancer. The proposed CAD-Skin system detects and classifies skin lesions using deep convolutional neural networks and autoencoders to improve the classification efficiency of skin cancer. The CAD-Skin system was designed and developed by the use of the modern preprocessing approach, which is a combination of multi-scale retinex, gamma correction, unsharp masking, and contrast-limited adaptive histogram equalization. In this work, we have implemented a data augmentation strategy to deal with unbalanced datasets. This step improves the model's resilience to different pigmented skin conditions and avoids overfitting. Additionally, a Quantum Support Vector Machine (QSVM) algorithm is integrated for final-stage classification. Our proposed CAD-Skin enhances category recognition for different skin disease severities, including actinic keratosis, malignant melanoma, and other skin cancers. The proposed system was tested using the PAD-UFES-20-Modified, ISIC-2018, and ISIC-2019 datasets. The system reached accuracy rates of 98%, 99%, and 99%, consecutively, which is higher than state-of-the-art work in the literature. The minimum accuracy achieved for certain skin disorder diseases reached 97.43%. Our research study demonstrates that the proposed CAD-Skin provides precise diagnosis and timely detection of skin abnormalities, diversifying options for doctors and enhancing patient satisfaction during medical practice.

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.

Melanoma, the deadliest type of skin cancer in humans, poses significant challenges while diagnosing skin lesions through thermoscopic imaging.These challenges include issues such as hair interference, inaccurate capturing of skin lesion shapes, and extraction of irrelevant features.However, overfitting occurs due to oversampling, resulting in erroneous skin cancer captures, and failure to detect noise and unwanted hair in melanoma images.The research proposes the Premature Convergence Strategy with Artificial Jelly Search Optimization (PCSAJSO) algorithm and Convolutional Neural Network (CNN) for classifying skin cancer into melanoma and non-melanoma.Initially, images are obtained from ISIC2018, ISIC2019, and PH2 datasets, used to analyze the performance of the proposed method.The W-Net method is utilized to segment skin cancer by capturing the contextual information from various scales in skin cancer images.The PCS technique ensures a balance between exploration and exploitation in search space which helps to increase convergence speed and adjusts population size for skin cancer classification.In comparison to the existing techniques like Inception-ResNet and U-Net, the PCSAJSO-CNN achieves commendable accuracies of 98.15%, 99.38%, 99.70%, 94.68%, and 97.08% respectively on the ISIC2016, ISIC2017, ISIC2018, ISIC2019 and PH2 datasets.

Skin cancer is one of the most common cancers, and its early detection can have a huge impact on its outcomes. Deep learning, especially convolutional neural networks, perform well in processing massive amounts of data, especially image data in classifying skin cancer. In this paper, convolutional neural networks are mainly used to diagnose and classify 7 types of skin lesions, including melanoma, basal cell carcinoma, melanocytic nevus, actinic keratosis, and intraepithelial carcinoma, benign keratinoid lesions, dermatofibroma, and vascular lesions. First, the characteristics of skin lesion images are analyzed, using image processing technology and sampling technology to preprocess skin lesion images. Then the training parameters of imageNet network are adjusted through the idea of transfer learning on InceptionV3, ResNet50, DenseNet201, and other networks to perform training classification. Furthermore, different convolutional neural network models are built for classification. In order to validate the classification performance of various convolutional neural network models, this paper adopts ISIC 2017 HAM10000 dataset for experiments. The experimental results show that proper preprocessing is necessary when applying CNN for image classification. In classifying the 224*224 skin lesion images, the classical deep convolutional network with DenseNet201 achieved a remarkable performance classification accuracy of 99.12% for training and 86.91% for testing.

Multiclass skin lesion localization and classification using deep learning based features fusion and selection framework for smart healthcare

Skin cancer is a major issue regarding public health and mostly affects the Caucasians every year. It is the leading form of cancer affecting the people of USA. The mortality rate of skin cancer can be reduced to 2% if detected early. Dermatoscopy is a very popular tool in the diagnosis of benign as well as cancerous pigmented skin lesions. Dermatoscopy images are also suitable for automated diagnosis of pigmented skin lesions using deep learning techniques. This espouses the need of building an easily accessible, portable, faster and cheaper diagnostic system. The existing works mainly use pretrained network and huge processing resource. In this work, a 16-layer deep convolutional neural network is developed from the scratch. The network consists of 9 convolutional layers, 4 maxpool layers and 3 fully connected layers. The validation of the proposed architecture is done on the dermatoscopic images of the Kaggle dataset archive without augmentation. The proposed work offers an accuracy value of 87.58% which is much better than the existing works on the Kaggle dataset without augmentation. skin cancer, Dermatoscopic Images, convolutional neural network, Kaggle.

Not knowing the type of skin lesion or too much-delayed diagnosis can lead to chronic disease or skin cancer. In SkinAid, we propose an application that can not only assist the dermatologist in obtaining a preliminary analysis of detecting, classifying and monitoring the skin lesion but also creates awareness for the user to care for the skin. SkinAid classifies and provides information on several skin lesions. In this regard, the highly unbalanced and limited data leads the existing Deep Learning models to overfit or poorly generalize, resulting in re-duced performance. We explore Generative Adversarial Networks (GANs) to augment and enhance the dataset. Also, we trained a Deep Convolutional Neural Network (CNN) model for edge computing platforms that can automatically detect and classify skin lesions from the captured image of the user's skin. A single-board computer and a smartphone employed as edge platforms have been implemented in SkinAid. The SkinAid model detects and classifies 7 different skin lesions with an overall accuracy of 92.2%. A health worker or patient can capture the real-time skin lesion images using a smartphone app camera to obtain a preliminary analysis to diagnose the skin lesion in Internet-of-Medical-Things (IoMT) platform for remote monitoring by dermatologists.

Understanding the learning mechanism of convolutional neural networks in spectral analysis

Every year, both in developed and developing nations, the prevalence of skin cancer rises. Due to a lack of resources and medical knowledge, diagnosing skin lesions is more difficult in third-world nations. Unusual growths or alterations in the skin are known as skin lesions, and they can result from a number of causes, including cancer, inflammatory diseases, infections, traumas, and heredity. Malignant (cancerous) and benign (non-cancerous) skin lesions are both possible. This disease is contracted when the pigments that produce skin color become cancerous. Dermatologists find it difficult to diagnose skin cancer since the pigments of various skin conditions might look identical. This led to the goal of this work, which is to use attention mechanisms to design a system for dermatological diagnoses of skin lesions. Modern network architectures ResNet and EfficientNet, enhanced with specially designed patch-based attention heads, are the approach used to accomplish this. The HAM10000 dataset, a thorough compilation of dermatoscopic pictures of typical pigmented skin lesions, was used in the investigation. In order to improve the model's capacity to recognize minute yet crucial variations among lesion types, attention heads were created to highlight and identify important characteristics within patches of the dermatoscopic pictures. According to the experimental results, the model that accurately classifies images into different lesion types had the lowest accuracy of 72% on a dataset of over 10,000 image instances, while the model that determines whether a lesion is cancerous or non-cancerous had the highest accuracy of 98%, demonstrating its robustness and reliability for...

Skin cancer is considered to be the most common human malignancy. Around 5 million new cases of skin cancer are recorded in the United States annually. Early identification and evaluation of skin lesions are of great clinical significance, but the disproportionate dermatologist-patient ratio poses a significant problem in most developing nations. Therefore a novel deep architecture, named as SkiNet, is proposed to provide faster screening solution and assistance to newly trained physicians in the process of clinical diagnosis of skin cancer. The main motive behind SkiNet’s design and development is to provide a white box solution, addressing a critical problem of trust and interpretability which is crucial for the wider adoption of Computer-aided diagnosis systems by medical practitioners. The proposed SkiNet is a two-stage pipeline wherein the lesion segmentation is followed by the lesion classification. Monte Carlo dropout and test time augmentation techniques have been employed in the proposed method to estimate epistemic and aleatoric uncertainty. A novel segmentation model named Bayesian MultiResUNet is used to estimate the uncertainty on the predicted segmentation map. Saliency-based methods like XRAI, Grad-CAM and Guided Backprop are explored to provide post-hoc explanations of the deep learning models. The ISIC-2018 dataset is used to perform the experimentation and ablation studies. The results establish the robustness of the proposed model on the traditional benchmarks while addressing the black-box nature of such models to alleviate the skepticism of medical practitioners by incorporating transparency and confidence to the model’s prediction.

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 one of the most common types of cancer in the world. Different computer-aided diagnosis systems have been proposed to tackle skin lesion diagnosis, most of them based in deep convolutional neural networks. However, recent advances in computer vision achieved state-of-art results in many tasks, notably Transformer-based networks. We explore and evaluate advances in computer vision architectures, training methods and multimodal feature fusion for skin lesion diagnosis task. Experiments show that PiT ($0.800 \pm 0.006$), CoaT ($0.780 \pm 0.024$) and ViT ($0.771 \pm 0.018$) backbone models with MetaBlock fusion achieved state-of-art results for balanced accuracy metric in PAD-UFES-20 dataset.