An Accurate Bitcoin Price Prediction using logistic regression with LSTM Machine Learning model
Volume-3 | Issue-3
Design of Distribution Transformer Health Management System using IoT Sensors
Volume-3 | Issue-3
Energy Management System in the Vehicles using Three Level Neuro Fuzzy Logic
Volume-3 | Issue-3
Cloud Load Estimation with Deep Logarithmic Network for Workload and Time Series Optimization
Volume-3 | Issue-3
Design of a Food Recommendation System using ADNet algorithm on a Hybrid Data Mining Process
Volume-3 | Issue-4
Review on Data Securing Techniques for Internet of Medical Things
Volume-3 | Issue-3
Automatic Diagnosis of Alzheimer’s disease using Hybrid Model and CNN
Volume-3 | Issue-4
Population Based Meta Heuristics Algorithm for Performance Improvement of Feed Forward Neural Network
Volume-2 | Issue-1
Comparative Analysis of an Efficient Image Denoising Method for Wireless Multimedia Sensor Network Images in Transform Domain
Volume-3 | Issue-3
A Comprehensive Review on Power Efficient Fault Tolerance Models in High Performance Computation Systems
Volume-3 | Issue-3
An Integrated Approach for Crop Production Analysis from Geographic Information System Data using SqueezeNet
Volume-3 | Issue-4
An Accurate Bitcoin Price Prediction using logistic regression with LSTM Machine Learning model
Volume-3 | Issue-3
Design of Distribution Transformer Health Management System using IoT Sensors
Volume-3 | Issue-3
Design of a Food Recommendation System using ADNet algorithm on a Hybrid Data Mining Process
Volume-3 | Issue-4
Automatic Diagnosis of Alzheimer’s disease using Hybrid Model and CNN
Volume-3 | Issue-4
Effective Prediction of Online Reviews for Improvement of Customer Recommendation Services by Hybrid Classification Approach
Volume-3 | Issue-4
Acoustic Features Based Emotional Speech Signal Categorization by Advanced Linear Discriminator Analysis
Volume-3 | Issue-4
Analysis of Statistical Trends of Future Air Pollutants for Accurate Prediction
Volume-3 | Issue-4
Identification of Electricity Threat and Performance Analysis using LSTM and RUSBoost Methodology
Volume-3 | Issue-4
Review on Data Securing Techniques for Internet of Medical Things
Volume-3 | Issue-3
Volume - 5 | Issue - 4 | december 2023
Published
06 December, 2023
Skin cancer is a significant threat to the global health, with over 2.1 million new cases diagnosed annually worldwide. Timely detection and treatment are vital for improving survival rates, yet the limited availability of dermatologists in remote regions poses a significant barrier. The utilization of Artificial Intelligence (AI) and Deep Learning (DL) has seen a remarkable surge in recent years for skin cancer prediction. This study conducts an in-depth review of advanced skin cancer prediction methods employing deep learning techniques and explores the diverse array of machine learning algorithms applied in this context. Skin cancer comprises seven distinct diagnoses, presenting a formidable challenge for dermatologists due to the overlapping phenotypic traits. Conventional diagnostic accuracy typically ranges from 62% to 80%, underscoring the potential of machine learning to enhance diagnosis and treatment. While some researchers have created binary skin cancer classification models, extending this to multiple classes with superior performance has been elusive. A deep learning classification model for various skin cancer types, yielding promising results that highlight the superiority of deep learning in classification tasks is developed. The experimental outcomes demonstrate that the individual accuracy of Sequential, ResNet50, DenseNet201, VGG-16 and EfficientNetB0 models are aggregated and yields the maximum occurring output value from all the models. Furthermore, a comparative analysis with the latest skin classification models underscores the superior performance of the proposed multi-type skin cancer classification model.
KeywordsHAM10000 Sequential ResNet50 DenseNet201 VGG16 EfficientNetB0
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