Improving Early Diagnosis: An AI-Powered Method for Precise Skin Cancer Identification

An important development in medical diagnostics is the use of artificial intelligence (AI) helps to investigate the creation and application of the system intended to detect skin cancer using pictures of skin lesions. The method uses Convolutional Neural Networks (CNN), a type of machine learning technique, to identify lesions as benign or malignant based on its analysis of dermatoscopic pictures. Expert dermatologists gather and annotate a sizable dataset of photos of skin lesions as the first step in the procedure. Preprocessing techniques such as segmentation, normalisation, and augmentation are used to these pictures in order to increase the analysis's focus and quality. The colour, texture, and form characteristics of the lesions are analysed using feature extraction techniques, which are essential for precise categorization. CNN gains the ability to identify trends and characteristics linked to various forms of skin cancer. After training, a different set of pictures is used to verify the model and evaluate its performance in terms of ROC-AUC (Receiver Operating Characteristic - Area Under Curve), accuracy, sensitivity, and specificity. After that, the AI system is incorporated into an intuitive user interface, giving dermatologists and medical professionals a trustworthy instrument for screening for skin cancer. The system's accessibility is further increased via a mobile application version, which makes initial screenings possible in underserved and rural places. Dense Net 201 performs best in training and test accuracy as well as training loss, despite being the deepest and having the most parameters. With fewer layers and parameters than Dense Net 201 and Inception V3, VGG16 performs admirably as well, particularly in training accuracy and loss. Finally, the Dense Net 201 performs the best in terms of training and test accuracy as well as training loss. Dense Net 201 is the best effective model if training time and computer resources are not limited.