The results demonstrate that deep learning techniques can significantly assist in early detection and classification of skin cancer, thereby supporting dermatologists in clinical decision-making and improving diagnostic efficiency and mortality rates associated with skin cancer.
Experimental results indicate that the proposed framework achieves high diagnostic accuracy while providing interpretable insights that assist dermatologists in understanding model predictions.
Choudhuri Saswat Pattnaik, Priyanka Shit, Subhashree Raul et al.· International Research Journ...· 0 citations
Skin cancer is a significant global health concern, where early and accurate detection is important for supporting effective treatment and improving patient outcomes. To perform manual examination of the skin lesions is time consuming and could rely significantly on clinical expertise, which may lead to the need of computer aided approaches helping to classify skin lesions. In this study, an automated, DL approach to classifying dermoscopic skin images as benign or malignant is proposed. In this research, a publicly available Kaggle skin cancer dataset was used, containing 3600 images with labels, half of which were malignant and the other half were benign. The data set was split into 70% training, 15% validation and 15% testing sets. The input images were preprocessed and enhanced using image processing and augmentation techniques to standardize images, enhance the diversity of training samples, and enhance model generalization. Transfer learning was explored in four different pretrained convolutional neural network (CNN) architectures. The models were trained under similar experimental conditions analysis were used for the evaluation. Of the evaluated architectures, MobileNetV2 had the best overall classification performance with an accuracy of 91.11%. The accuracy of DenseNet201 was 88.06%, higher than that of ResNet50V2 (86.81%) and Xception (85.83%). To explore the learning behavior of the models, training and validation accuracy and loss curves were also analyzed, in addition to the quantitative evaluation. Furthermore, an explainable artificial intelligence method named Grad-CAM was added to visualize the image regions that the model relied on to make its prediction, thus gaining another insight into the model's decision-making behavior of the CNN architectures. The results show that both the CNN architectures used for the experiment and the automated classification of skin lesions using them are effective, and the MobileNetV2 performs the best in the experimental framework of this study. The proposed framework can serve as a foundation for the development of efficient and interpretable computer-aided skin lesion classification systems.
Raees Adnan, Fawad Nasim, Muqaddas Salahuddin· SOCIAL PRISM· 0 citations
Skin diseases represent one of the most widespread categories of health disorders worldwide, and timely diagnosis plays a critical role in preventing complications such as skin cancer. Conventional diagnostic procedures depend largely on visual examination by dermatologists, a process that is subjective, time-consuming, and difficult to access in rural or under-resourced regions. This paper presents a comprehensive deep learning-based framework for the automated detection and classification of skin diseases from dermoscopic and clinical images. The proposed system employs a transfer-learning approach built on the ResNet50 convolutional neural network architecture, pre-trained on ImageNet and fine-tuned on benchmark dermatological datasets, namely HAM10000, the ISIC Archive, and DermNet. The methodology encompasses dataset collection, image pre-processing, data augmentation, feature extraction, model training, disease classification, and rigorous performance evaluation. ResNet50 is selected for its residual-learning capability, which mitigates the vanishing-gradient problem and enables deeper, more accurate networks suited to fine-grained medical image analysis. An extensive review of over thirty related studies spanning convolutional architectures, ensemble methods, and emerging transformer-based models is used to position the proposed framework within the current state of the art. The framework is evaluated using standard classification metrics, including accuracy, precision, recall, specificity, F1-score, Cohen’s kappa, and confusion-matrix analysis, and is benchmarked conceptually against alternative architectures such as a baseline CNN, VGG16, MobileNetV2, DenseNet, and EfficientNet. The anticipated outcome is an accurate, scalable, and accessible screening tool capable of assisting healthcare professionals in early diagnosis, thereby reducing diagnostic delay and improving healthcare accessibility, particularly in regions with limited dermatological expertise.
Nisha Rajodiya, Shailendra Mishra, Sumitra Menaria et al.· International Journal of Res...· 0 citations
Skin diseases represent a significant global health concern, requiring accurate and timely diagnosis for effective treatment. This paper presents a deep learning-based approach for classification of skin diseases using medical image analysis. The proposed system uses Convolutional Neural Networks (CNNs) and related deep learning techniques to automatically learn discriminative visual features from skin-lesion images and classify disease categories. Image preprocessing and data augmentation are used to improve consistency and robustness. The workflow covers image acquisition, preprocessing, augmentation, model training, validation, testing, and performance analysis. The system is intended to assist healthcare professionals by providing rapid image-based decision support while reducing dependence on purely manual screening. Standard measures such as accuracy, precision, recall, F1-score, and confusion-matrix analysis are considered for evaluation. The approach demonstrates the potential of artificial intelligence in dermatology while recognizing that dataset quality, external validation, interpretability, and clinical supervision remain essential for responsible deployment.
S. Behera, Sri Bapuji Bismaya Kumar Giri, Subham Singh Mundari et al.· International Research Journ...· 0 citations
Early and accurate detection of skin cancer, particularly melanoma, remains a critical challenge in computer-aided diagnosis, motivating the development of reliable and interpretable machine learning solutions. This study presents a comparative algorithmic analysis of deep learning models for automated skin cancer detection using dermoscopic images. Specifically, convolutional neural networks (CNNs) and Vision Transformers (ViTs) are implemented within a unified framework, employing transfer learning and standardized preprocessing techniques on a benchmark dataset. The proposed methodology incorporates data augmentation and class imbalance handling strategies, while model performance is evaluated using clinically relevant metrics, including accuracy, precision, recall, F1-score, and area under the ROC curve. In addition, explainability techniques such as Grad-CAM and attention visualization are employed to enhance model interpretability, and decision threshold analysis is conducted to assess trade-offs between sensitivity and specificity in melanoma detection. Experimental results demonstrate that CNN-based architectures achieve robust performance in capturing local spatial features, while transformer-based models provide competitive results through global contextual representation. However, variations are observed in model calibration and false-negative rates, which are critical for clinical deployment. Overall, the findings highlight the importance of combining algorithmic performance with interpretability and threshold optimization to support reliable and clinically meaningful computer-aided diagnosis systems.
Melanoma is a type of skin cancer that is one of the most aggressive, and its diagnosis requires quick and accurate identification to increase patient survival. This paper draws a comparative analysis of advanced deep learning systems, namely, VGG and traditional CNN systems, used in automatic melanoma detection in dermoscopic images. The techniques included preprocessing of dermoscopic sample datasets, training of both VGG and baseline CNN, and the testing of their classification by confusion matrices and receiver operating characteristic (ROC) analysis. The results showed that VGG model was more inclined to have high true positive rate of melanoma, and had 20/20 correct melanoma and 0.92 area under ROC curve (AUC) in its confusion chart. The CNN model did a little better, with a slightly higher AUC of 0.95 and lower false negative percentages at normal. These results show that deep learning models can be used to advance clinical and face-to-face diagnosis of melanoma; both models provide strong performance.
A. S.· International Conference on...· 0 citations
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