Melanoma Skin Cancer and Nevus Mole Classification using Intensity Value Estimation with Convolutional Neural Network
This work addresses the challenge of early melanoma detection for medical diagnosis, but it is incremental as it builds on existing CNN methods with a focus on intensity features.
The paper tackled the problem of detecting and classifying melanoma skin cancer and nevus moles at immature stages by developing an automatic deep learning system using intensity value estimation with a convolutional neural network, achieving accuracy of 92.58%, sensitivity of 93.76%, specificity of 91.56%, and precision of 90.68%.
Melanoma skin cancer is one of the most dangerous and life-threatening cancer. Exposure to ultraviolet rays may damage the skin cell's DNA, which causes melanoma skin cancer. However, it is difficult to detect and classify melanoma and nevus mole at the immature stages. In this work, an automatic deep learning system is developed based on the intensity value estimation with a convolutional neural network model (CNN) to detect and classify melanoma and nevus mole more accurately. Since intensity levels are the most distinctive features for object or region of interest identification, the high-intensity pixel values are selected from the extracted lesion images. Incorporating those high-intensity features into the CNN improves the overall performance of the proposed model than the state-of-the-art methods for detecting melanoma skin cancer. To evaluate the system, we used 5-fold cross-validation. Experimental results show that a superior percentage of accuracy (92.58%), sensitivity (93.76%), specificity (91.56%), and precision (90.68%) are achieved.