Multiclass Skin Cancer Classification Using Ensemble of Fine-Tuned Deep Learning Models

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.

In the last few years, Deep Learning (DL) has been showing superior performance in different modalities of bio-medical image analysis. Several DL architectures have been proposed for classification, segmentation, and detection tasks in medical imaging and computational pathology. In this paper, we propose a new DL architecture, the NABLA-N network (∇N-Net), with better feature fusion techniques in decoding units for dermoscopic image segmentation tasks. The ∇N-Net has several advances for segmentation tasks. First, this model ensures better feature representation for semantic segmentation with a combination of low to high-level feature maps. Second, this network shows better quantitative and qualitative results with the same or fewer network parameters compared to other methods. In addition, the Inception Recurrent Residual Convolutional Neural Network (IRRCNN) model is used for skin cancer classification. The proposed ∇N-Net network and IRRCNN models are evaluated for skin cancer segmentation and classification on the benchmark datasets from the International Skin Imaging Collaboration 2018 (ISIC-2018). The experimental results show superior performance on segmentation tasks compared to the Recurrent Residual U-Net (R2U-Net). The classification model shows around 87% testing accuracy for dermoscopic skin cancer classification on ISIC2018 dataset.

One of the most common causes of death worldwide is skin cancer and its incidence is increasing. To achieve optimal treatment and improve clinical outcomes for patients, precision skin cancer detection and classification approaches are required, which can be achieved through the application of feature extraction and machine learning algorithms. The development of such algorithms to identify important patterns from skin cancer image datasets enables early detection and more accurate classification and more effective treatment. Although previous studies have tried to detect skin cancer using feature extraction techniques such as HFF, HOG, and GLCM, some weaknesses still need to be improved. This research aims to combine various feature extraction methods such as Gray Level Co-occurrence Matrix, Histogram Oriented Gradients, and Local Binary Patterns and machine learning algorithms such as Support Vector Machine, Random Forest, and Gaussian Naïve Bayes in the classification process between Melanoma and Nevus skin cancers. In this research, the number of datasets used is 17,397 derived from the ISIC Dataset. The results showed that the Histogram Oriented Gradients method with Support Vector Machine algorithm achieved the highest accuracy of 92%. The combination of Gray Level Co-occurrence Matrix and Local Binary Patterns with Random Forest algorithm also achieved an accuracy of 92%, the combination of Gray Level Co-occurrence Matrix, Histogram Oriented Gradients, and Local Binary Patterns with Random Forest algorithm also resulted in an accuracy of 92%. These findings suggest that the combination of various feature extraction methods and machine learning algorithms can improve accuracy in skin cancer classification, which in turn can contribute to early detection and more effective treatment.

Skin cancer is a significant global health concern, with timely and accurate diagnosis playing a critical role in improving patient outcomes. In recent years, computer-aided diagnosis systems have emerged as powerful tools for automated skin cancer classification, revolutionizing the field of dermatology. This survey analyzes 107 research papers published over the last 18 years, providing a thorough evaluation of advancements in classification techniques, with a focus on the growing integration of computer vision and artificial intelligence (AI) in enhancing diagnostic accuracy and reliability. The paper begins by presenting an overview of the fundamental concepts of skin cancer, addressing underlying challenges in accurate classification, and highlighting the limitations of traditional diagnostic methods. Extensive examination is devoted to a range of datasets, including the HAM10000 and the ISIC archive, among others, commonly employed by researchers. The exploration then delves into machine learning techniques coupled with handcrafted features, emphasizing their inherent limitations. Subsequent sections provide a comprehensive investigation into deep learning-based approaches, encompassing convolutional neural networks, transfer learning, attention mechanisms, ensemble techniques, generative adversarial networks, vision transformers, and segmentation-guided classification strategies, detailing various architectures, tailored for skin lesion analysis. The survey also sheds light on the various hybrid and multimodal techniques employed for classification. By critically analyzing each approach and highlighting its limitations, this survey provides researchers with valuable insights into the latest advancements, trends, and gaps in skin cancer classification. Moreover, it offers clinicians practical knowledge on the integration of AI tools to enhance diagnostic decision-making processes. This comprehensive analysis aims to bridge the gap between research and clinical practice, serving as a guide for the AI community to further advance the state-of-the-art in skin cancer classification systems.

Context: Skin cancer is one of the most common and lethal forms of cancer if not diagnosed early. Detection at an early stage can mean a world of difference in survival. Despite advances in technology, most areas continue to face a lack of available dermatology specialists, making timely diagnosis a challenge. This gap between the need for early detection and the limited availability of experts highlights the urgency for automated, reliable diagnostic tools. Objective: In response to this need, the purpose of this work is to create a fast and accurate system for skin cancer detection that distinguishes between malignant and benign lesions. Method: This study, based on a Convolutional Neural Network (CNN), addresses some challenges, including limited data, image quality issues, and classification. Results: A balanced training set was used to train the model, which achieved a test accuracy of 88.60% and a macro-averaged Receiver Operating Characteristic Area Under the Curve (ROC-AUC) score of 0.95. Other significant metrics, such as precision, recall, and F1-score also validated the performance of the model. We developed a web-based interface to enable practical deployment and usability for real-world applications. Conclusions: The findings demonstrate the potential of deep learning techniques to enhance skin cancer detection and serve as a point of reference for future research on multi-class classification and real-time diagnostic platforms.

Skin cancer is a widely spreading cause of mortality among the people specifically living on or near the equatorial belt. Early detection of skin cancer significantly improves the recovery prevalence and the chance of surviving. Without the assist of computer-aided decision (CAD) system, skin cancer classification is the challenging task for the dermatologist to differentiate the type of skin cancer and provide the suitable treatment. Recently, the development of machine learning and pretrained deep neural network (DNN) shows the tremendous performance in image classification task which also provide the promising performance in medical field. However, these machine learning methods cannot get the deep features from network flow which resulting in low accuracy and the pretrained DNN has the complex network with a huge number of parameters causes the limited classification accuracy. This paper focuses on the classification of skin cancer to identify whether it is basal cell carcinoma, melanoma or squamous cell carcinoma by using the development of hybrid convolutional neural network algorithm and autoregressive integrated moving average model (CNN-ARIMA). The CNNARIMA model was trained and found to produce the best accuracy of 92.25%.

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 is widespread and can be potentially fatal. According to the World Health Organisation (WHO), it has been identified as a leading cause of mortality. It is essential to detect skin cancer early so that effective treatment can be provided at an initial stage. In this study, the widely-used HAM10000 dataset, containing high-resolution images of various skin lesions, is employed to train and evaluate. Our methodology for the HAM10000 dataset involves balancing the imbalanced dataset by augmenting images followed by splitting the dataset into train, test and validation set, preprocessing the images, training the individual models Xception, InceptionResNetV2 and MobileNetV2, and then combining their outputs using fuzzy logic to generate a final prediction. We examined the performance of the ensemble using standard metrics like classification accuracy, confusion matrix, etc. and achieved an impressive accuracy of 95.14% and the result demonstrates the effectiveness of our approach in accurately identifying skin cancer lesions. To further assess the efficiency of the model, additional tests have been performed on the DermaMNIST dataset from the MedMNISTv2 collection. The model performs well on the dataset and transcends the benchmark accuracy of 76.8%, achieving 78.25%. Thus the model is efficient for skin cancer classification, showcasing its potential for clinical applications.

Skin cancer diagnosis faces challenges in efficiency and accuracy. This research addresses the need for improved non-invasive diagnostic tools by leveraging deep learning for multi-class skin cancer classification from dermoscopic images. A key focus is overcoming the limitations of imbalanced datasets, common in medical imaging, which can hinder model performance. We propose an optimal strategy utilizing a Convolutional Neural Network (CNN) transfer learning methodology. The process involves CNN-based segmentation to isolate relevant regions, followed by feature extraction and classification. We comparatively evaluated three pre-trained transfer learning techniques: DenseNet201, ResNet50, and VGG16, using the HAM10000 dataset (10,015 images across seven skin cancer classes). To mitigate severe class imbalance, Random Oversampling was employed, chosen for its simplicity and effectiveness in balancing the dataset and enhancing model generalization. Model performance was rigorously evaluated using accuracy, precision, recall, and F1-score. DenseNet201 consistently achieved superior performance, with an accuracy of 97% post-oversampling. It also exhibited the highest precision, recall, and F1-score across all models, confirming its effectiveness in classifying both majority and minority classes. Compared to previous studies on HAM10000, our DenseNet201 model's test accuracy of 96.52% is competitive or superior to reported accuracy of 90-92%. This highlights the synergistic effect of DenseNet201's efficient feature reuse and robust data balancing. This research provides a robust framework for advanced methodologies in skin cancer classification, particularly for imbalanced medical image datasets.

Skin cancer is one of the most common and lethal diseases in America, and its early detection and treatment is the best approach to protect the lives of those afflicted. Computer-Aided Diagnosis systems have been implemented for decades as evidence of intelligent methods applied in the medical field. In recent years, machine and deep learning fields have shown great potential in medical diagnosis, particularly in skin cancer classification. These methods enable the automated extraction of complex input features, such as those in medical imagery. Therefore, with the advent of quantum computing, it is now possible to perform complex computations with increased speed and efficiency. This study explores the application of quantum machine learning in the classification of skin cancer using the ResNet50 model, a deep convolutional neural network for RGB images. The research employs a quantum-enhanced version of ResNet50 using a quanvolutional layer that the input skin lesion images go through and compare its performance with the classical ResNet50 version. We show comparative experiments, and the results indicate that further experiments are needed using more extensice datasets and different quantum deep learning architectures.

Skin cancer continues to be a global health issue, with early detection being critical for improving treatment outcomes. While deep learning models like convolutional neural networks (CNNs) have proven highly efficient in skin cancer classification from dermatological images, they often disregard the valuable patient metadata that can contribute to better diagnostic accuracy. In the present study, we introduce a multimodal late fusion framework that integrates both skin cancer images and patient metadata. The approach leverages the Inception-ResNet-v2 (IRv2) model to extract image features, and a stacking ensemble model consisting of Extra Trees and Random Forest classifiers for patient metadata preprocessing. Then, a final voting classifier applying a soft voting strategy, which aggregates class probabilities from Logistic Regression and Random Forest as base voters, is employed for the final fusion methodology. This leads to an accuracy of 95.9% on the HAM10000 dataset. Our results highlight the potential of multimodal approaches in healthcare applications.

Skin cancer is the most common type of cancer, a life-threatening condition that leads to serious health problems if not detected early, and its incidence is increasing worldwide. In recent years, computer vision and decision support systems have been used for disease detection in dermoscopic images. Furthermore, it has been observed that data representation methods affect the detection performance of these models, and the effect of color information on transformer-based models has not been sufficiently investigated. This study used the International Skin Imaging Collaboration (ISIC) 2017 dataset consisting of RGB images, and these images were converted into the HSV, LAB, and YCbCr color spaces. Transformer-based models, including visual transformer (ViT), swin transformer, data efficient image transformer (DeiT), and label-free self-distillation (DINO), were used for benign and malignant classification. According to the classification performance results, RGB and HSV color spaces particularly in the DeiT and Swin models, stable and high accuracy values ​​were obtained. It was observed that the ViT and DINO models were more sensitive to color space transformations and achieved lower classification performance compared to other models. The highest performance in skin cancer classification was achieved with the DeiT model trained in the RGB color space, with the highest accuracy (0.7668). Furthermore, the explainability-based gradient-weighted class activation mapping (Grad-CAM) method was used to analyze where the models focused in image regions when making classification decisions. This study shows the effect and usability of color space transformations in transformer-based models for skin cancer classification and offers a comparative contribution to the literature.

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 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.