Dermoscopic Image Classification for Skin Cancer Diagnosis Using Deep Learning and Cuckoo Search With 3D Shearlet

Melanoma, the most serious kind of skin cancer, is formed by a mutation in melanocytes.An early diagnosis is very important to reduce mortality.The proposed system categorizes dermoscopic images for identifying skin malignancies using Deep Learning and Cuckoo Search (DLCS) in conjunction with 3D Shearlet Transform (3DST).There are four different modules that make up the DLCS system.These modules include preprocessing, representation of dermoscopic images, selection of directional sub-bands and features, and classification.Using a straightforward median filtering strategy, the initial step eliminates the undesirable information which degrades the system's performance.These details include noise and hair in skin images.The pre-processed image is decomposed using 3D ST during the feature extraction step to retrieve the textural characteristics at varying scales and directions.The DLCS technique is used to choose a certain proportion of features, and then, a straightforward DL architecture with ten hidden layers is used to create a classification system for the dermoscopic image.Experimental results on PH 2 and ISIC databases show that the DLCS-3DST system's performances are affected by the features from different Levels (L) and Directions (D).Training the classifier using the selected features from 3L-8D provides the highest accuracy of 99.22% for PH 2 database and 99.39% for ISIC database.It is also observed that when dermoscopic images are decomposed by 4L with 32D, there is an increase in redundant information, which negatively impacts the performance of the classifier.