Enhance Skin Cancer Deep Learning-Based Categorization: A Thorough Examination and Comparative Study

Abstract: One of the most prevalent types of cancer in the world is skin cancer, which includes melanoma, squamous cell carcinoma, and basal cell carcinoma. While increasing survival rates requires early discovery and treatment, conventional diagnostic techniques frequently have subjectivity and inconsistent results. The purpose of this study is to better understand how deep learning techniques, namely convolutional neural networks (CNNs), might improve the detection of skin cancer. Using the ISIC dataset, eight advanced models—DenseNet121, InceptionV3, ResNet50V2, VGG16, VGG19, InceptionResNetV2, Xception, and CNN—were trained and verified to categorize different types of skin lesions. The outcomes highlight the superior accuracy that models like as Xception and InceptionV3 can accomplish, highlighting their potential for accurate and timely detection. This investigation assesses how well different deep learning models diagnose different skin diseases. Models like Densenet121, InceptionV3, and VGG16 are compared based on metrics like accuracy, recall, precision, and F1-score. When it comes to multi-classification, InceptionResNetV2 and VGG16 stand out as the best performers with 0.9547 accuracy, while Custom CNN performs the worst with 0.8040. A thorough comparison of the accuracy, precision, recall, and F1-score performance indicators identifies the unique advantages and disadvantages of each model. This study demonstrates how artificial intelligence (AI) is revolutionizing medical imaging by offering a reliable, unbiased, and highly accurate method for detecting skin cancer. The integration of deep learning models into clinical practice is made possible by these discoveries, which might enhance patient outcomes and diagnostic accuracy in dermatology. IndexTerms – Python, Machine Learning, Deep Learning, Skin Cancer, Deep Learning Algorithms, Medical Imaging, Neural Network Models.