Aug 2026· International Conference on Information Security and Cryptology· pp. 1190-1195· 0 citations· 16 references
Abstract
Early detection is important for skin cancer as it can increase both the life expectancy of patients and the likelihood that their acquired treatments will work. This study presents a novel deep-learning-based framework called LesionNet- FCL, which was created to categorise types of skin lesions. The work utilises EfficientNet-B0 as a feature extractor by extracting discriminative features from dermoscopic images passed as inputs to a classifier that is a Fully Connected Layer (FCL). Regarding the evaluations, the model will be tested using the ISIC data set with all data obtained from Kaggle. To further support the evaluation processes, the study also included many pretrained CNN's with the same experimentation conditions, ultimately resulting in LesionNet- FCL outperforming all of the other models by generating an overall classification accuracy of 96%. The overall results indicate that combining the process's optimized classification method with the use of deep features will generate superior results than an individual model. Therefore, supporting the need for dermatologists to have reliable computer-aided diagnostic assistance as a means to improve still-image-based clinical decision-making and to enable faster identifications.
An explainable deep learning framework for skin lesion classification (EDLF-SLC) is developed and makes use of various approaches in machine learning and explainable artificial intelligence to ensure improvements in terms of both accuracy and interpretability in a three-stage manner.
Hasan Hashim, M. Rokaya, Mohammed Farsi et al.· Journal of Visualized Experi...· 0 citations
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.
Raees Adnan, Fawad Nasim, Muqaddas Salahuddin· SOCIAL PRISM· 0 citations
Skin cancer has emerged as one of the fastest-growing diseases across the globe, and hence, timely and reliable identification is crucial for increasing survival rates. In the past few years, deep neural network automated analysis has been found to be beneficial in aiding doctors in diagnosing skin cancer by providing...
Gogireddy Nandini, Potarlanka Kumudvi, Ganji Aparna et al.· 2026 International Conferenc...· 0 citations
Pigmented skin lesions must be detected early and accurately: the chances of survival from melanoma are greater than 90% if it is detected early. Manual dermoscopic analysis is, however, time-consuming and subjective. In this paper, the authors propose a novel network-level fusion deep learning model with Explainable A...
Fatima Abbas, Iftikhar Naseer· International Journal of Inn...· 0 citations
This paper presents a comprehensive deep learning-based framework for the automated detection and classification of skin diseases from dermoscopic and clinical images, built on the ResNet50 convolutional neural network architecture and evaluated using standard classification metrics.
Nisha Rajodiya, Shailendra Mishra, Sumitra Menaria et al.· International Journal of Res...· 0 citations
The early diagnosis of oral cancer is critical when it comes to enhancing the treatment process as well as increasing chances of survival. Unfortunately, there are challenges in designing automated classifiers since clinical image datasets with sufficient annotations can be scarce. In this work, an ensemble deep learni...
M. Shuaib, Ayushi Kumari· 2026 6th International Confe...· 0 citations
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