Author(s): Ahmed Al-Ghanimi

Abstract: Skin cancer is a prevalent and possibly dangerous disorder that requires accurate classification and early discovery in order to be properly treated. This article explores the application of convolutional neural networks (CNNs), a state-of-the-art method, to address these problems. The goal of this research is to develop a trustworthy CNN-based model that can recognize lesions associated with skin cancer and accurately classify them into many categories, including benign moles, malignant melanomas, squamous cell carcinomas, and basal cell carcinomas. In order to do this, we will gather a substantial set of dermatological images representative of various racial backgrounds, age groups, and skin types. The trained CNN model will undergo a thorough evaluation utilizing a number of measures, including area accuracy, precision, recall, and F1-score. A very accurate skin cancer detection and classification algorithm, with the potential to revolutionize dermatological diagnosis, is one of the predicted outcomes of this study. Our method may lead to better patient outcomes by providing early and accurate skin cancer type identification. The results of this study have important ramifications for dermatology and healthcare, opening the door to the creation of automated diagnostic tools that may be included in mobile apps and telemedicine platforms for increased accessibility. The effect of this innovative method for skin cancer detection and classification will be further increased by future work that involves collaboration with dermatologists for clinical validation and practical deployment.

DOI: 10.33545/27076636.2024.v5.i1a.89

Pages: 31-37 | Views: 123 | Downloads: 75

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