A Novel Deep Learning Framework for MultiClass Skin Cancer Classification Using ModifiedStacked Capsule Network

Abstract

Skin cancer is one of the deadliest kinds of cancer because of it’s due to its visible onset and the potential forrapid progression and spread. Oncologists employ several techniques, including imaging and biopsies, to determinewhether skin cancer is present, but these are labor-intensive and time-consuming. Developing an automated andaccurate framework is essential for early skin cancer detection, which greatly increases the probability of a successfultreatment and recovery. Therefore, to address the aforementioned problems, this study aim to propose a novel DeepLearning (DL)-based multi-class skin cancer classification (MC-SC2) model for automatically diagnosing skin cancersusing dermoscopic images. Initially, the dermoscopic images are preprocessed using a Gaussian Filter (GF) toeffectively remove noise. To address the class imbalance issue, we applied advanced augmentation techniques tooversample minority classes, ensuring a uniform class distribution and enhancing the model's ability to generalize. Toextract the complex and dominant features, we propose a novel DL-based model called BAM-EfficientNet (BottleneckAttention Module with EfficientNetB7). In BAM-EfficientNet, we replace each Squeeze-and-Excitation (SE) attentionmodule with a BAM in the traditional EfficientNetB7; this modification enables the network to concentrate on the mostrelevant regions in the images. The extracted features fed into the proposed Modified Stacked Capsule Network(MSCNet) to classify a skin lesion image as AKIEC, BCC, BKL, DF, MEL, NV, and VASC. In the proposed MSCNetemploys the Disperse Dynamic Routing (DDR) algorithm to improve capsule networks performance, and network'sinitial weights and biases are fine-tuned using Pelican Optimization Algorithm (POA) to further enhance performance.The HAM10000 dataset is used to assess the suggested model. The findings indicate that our approach beats currentmethods with an accuracy of 99.21% in classifying the seven different types of skin cancer, yielding substantialoutcomes. These results demonstrate the potential of the suggested model as a quick, precise, and useful tool for earlyskin cancer diagnosis, providing important assistance in diagnosing skin cancer for medical professionals.