A Perspective Study on Scalable Computation Model for Skin Cancer Detection: Advancements and Challenges

Abstract

In the realm of scalable computing, the quest for early detection of skin cancer takes on a new dimension, demanding robust and efficient algorithms capable of handling vast amounts of data. This article delves into the burgeoning field of intelligent computing, where scalable solutions are imperative for processing the multitude of skin lesion images generated daily. Leveraging cutting-edge deep learning and machine learning techniques, researchers strive to develop automated systems capable of swiftly analyzing lesion features like symmetry, color, size, and shape.Through a comprehensive literature review, this paper explores the strides made in skin lesion detection, focusing on scalable computing approaches that accommodate the growing volume of medical imaging data. By identifying significant contributions in classification and segmentation methods, the article not only sheds light on the latest advancements but also offers guidance for aspiring researchers navigating the complexities of skin lesion analysis. Ultimately, the fusion of scalable computing and intelligent algorithms holds promise in revolutionizing early detection efforts, potentially saving countless lives by swiftly identifying and treating skin cancer at its onset.