Artificial intelligence-assisted detection model for melanoma diagnosis using deep learning techniques

Abstract

The progressive depletion of the ozone layer poses a significant threat to both human health and the environment. Prolonged exposure to ultraviolet radiation increases the risk of developing skin cancer, particularly melanoma. Early diagnosis and vigilant monitoring play a crucial role in the successful treatment of melanoma. Effective diagnostic strategies need to be implemented to curb the rising incidence of this disease worldwide. In this work, we propose an artificial intelligence-based detection model that employs deep learning techniques to accurately monitor nevi with characteristics that may indicate the presence of melanoma. A comprehensive dataset comprising 8598 images was utilized for the model development. The dataset underwent training, validation, and testing processes, employing the algorithms such as AlexNet, MobileNet, ResNet, VGG16, and VGG19, as documented in current literature. Among these algorithms, the MobileNet model demonstrated superior performance, achieving an accuracy of %84.94 after completing the training and testing phases. Future plans involve integrating this model with a desktop program compatible with various operating systems, thereby establishing a practical detection system. The proposed model has the potential to aid qualified healthcare professionals in the diagnosis of melanoma. Furthermore, we envision the development of a mobile application to facilitate melanoma detection in home environments, providing added convenience and accessibility.

A harmonic oscillator model of atmospheric dynamics using the Newton-Kepler planetary approach

“…At both the micro-level and the macro-level, natural phenomena can indeed exhibit more complexity than any simple but elegant mechanism alone can aptly capture. For example, the above-mentioned sigmoid saturation model, in contrast to a model of oscillators that shows periodicity, was successfully utilized as the activation function in a study of artificial intelligence-assisted detection methods of melanoma performed by Orhan and Yavşan [8]. In the study, the researchers chose the sigmoid function as the activation function in testing six classification algorithms' accuracy in melanoma detection models built on the convoluted neural network or CNN-based deep learning AI's.…”

“…In the study, the researchers chose the sigmoid function as the activation function in testing six classification algorithms' accuracy in melanoma detection models built on the convoluted neural network or CNN-based deep learning AI's. Among the six algorithms thus trained, the MobileNet algorithm achieved an accuracy rate of 89.4% in cancer detection [8]. In another study, Joshi et al [9] considered a mathematical model that is framed to investigate the role of buffer and calcium concentration on fibroblast cells.…”

Bifurcation analysis of a discrete-time prey-predator model

“…The combination of a thorough bifurcation analysis, a comprehensive literature review, and the application of numerical continuation techniques establish valuable insights into the intricate dynamics of predator-prey interactions and their implications for ecological management. Mathematical modeling and bifurcation analysis do not find application only in the field of mathematical ecology such as predator-prey modeling [6][7][8][9][10][11][12][13], but are widely used to derive dynamic process models in the field of mathematical epidemiology such as disease modeling [14][15][16][17][18][19][20][21][22][23][24][25][26][27][28], artificial intelligence [29][30][31][32][33], and other fields of science and technology [34][35][36][37][38][39][40][41]. Researchers continuously work in the field of mathematical ecology to present the challenges and suggest solutions to help future researchers of the respective field understand the existing research gaps.…”

Deep Learning Based Temperature and Humidity Prediction

“…Deep learning, also known as deep neural networks, is a machine learning technique that exhibits characteristics similar to the human brain, such as observation, analysis, learning, and decision-making [6]. It can perform tasks like data classification, transformation, and feature identification.…”