The Smart deep learning based Model for Early Detection and Diagnosis of Melanoma
Abdul Sajid Mohammed, Anuteja Reddy Neravetla, Sana Samreen, Nasmin Jiwani, J. Logeshwaran
Abstract
Melanoma, the deadliest form of skin cancer, is one of the most rapidly increasing cancer types. Early detection and diagnosis of melanoma can significantly reduce the impact of the disease on patient outcomes. However, traditional diagnostic approaches often only detect melanoma in the later stages, which limits the efficacy of treatment. In this context, a smart deep learning-based model for early detection and diagnosis of melanoma is proposed. The model consists of a convolutional neural network (CNN) and a visualization network. The CNN extract morphological features from dermoscopy images of moles, while the visualization network provides pixel-level accuracy and sensitivity for detection of potential malignancy. The model is trained and validated on clinical datasets. The model has achieved superior results than existing methods, providing a viable solution for early detection and diagnosis of melanoma. The model showed great potential in aiding dermatologists in the early detection and diagnosis of melanoma, leading to better patient outcomes and reduced mortality rates. The use of deep learning technology allowed for efficient processing of large amounts of data, leading to fast and accurate diagnoses. Additionally, the model was able to learn and improve over time, making it a valuable tool in detecting and diagnosing melanoma.