A proposed architecture for convolutional neural networks to detect skin cancers

Abstract

The goal of the research paper is to design and development of a computer-based system for the segmentation and classification of malignant skin diseases and a comparison between the accuracy of their detection, as two malignant diseases of skin diseases were detected. Namely, basal cell carcinoma and melanoma separately with images of nevus, and the images were collected from the ISIC 2020 archive group, as the total, The images used: 17,846 images include 3,008 images of basal cell carcinoma (BCC), 5,272 images of melanoma, and 9,566 images of a nevus, and validation data contains 20% of the images used which are not classified and randomly taken from the set of images, and the final test data contains 1,500 anonymous images. An architecture for the convolutional neural network technology in deep learning has been proposed that consists of a set of layers for processing. Processing raw input images for a group of pre-treatment transformations, the data augmentation process, so the number of images used became 86094 images of nevus, 27,072 images of BCC, and 47,448 images of melanoma. Through the detection process, the classification and detection accuracy of BCC was 98.25%, which is higher than the classification accuracy of melanoma is 91.61%.

Partitioning intensity inhomogeneity colour images via Saliency-based active contour

“…In contrast, selective segmentation attempts to segment a targeted object with minimal user intervention. There is enormous potential in applying this technique in tandem with the fields of medical imaging [24], [25] and biometric identification [26]. Several ACM based selective ISSN: 2088-8708 …”

Toward enhanced skin disease classification using a hybrid RF-DNN system leveraging data balancing and augmentation techniques

“…Recent advances in machine learning algorithms have created new opportunities for medical diagnosis, particularly in the classification of cutaneous diseases [3]. The purpose of this study is to propose a novel and effective composite system for the classification of skin diseases that incorporates the strengths of two potent machine learning techniques: the random forest (RF) model and the deep neural network (DNN) [4], [5]. Both algorithms offer distinct benefits that, when combined can improve diagnostic accuracy and overall system performance [6].…”

A Novel Levy Walk-Based Framework for Scheduling Power-Intensive Mobile Edge Computing Tasks

“…This characteristic has propelled CNNs to achieve remarkable breakthroughs in image analysis and feature extraction, bestowing upon them the ability to discern and efficiently classify features in images. Moreover, CNNs are renowned as shift-invariant artificial neural networks, a nomenclature that accentuates their capability to classify input information based on its hierarchical arrangement [22].…”

“…The hierarchical architecture of CNNs empowers them to process and extract features from input data in a shiftinvariant manner [22]. This implies that CNNs can adeptly recognize and classify objects within images, irrespective of their position or orientation.…”