Diabetic Retinopathy Detection Using Machine Learning
Volume-4 | Issue-1
Monocular Depth Estimation using a Multi-grid Attention-based Model
Volume-4 | Issue-3
Speedy Image Crowd Counting by Light Weight Convolutional Neural Network
Volume-3 | Issue-3
Construction of Efficient Smart Voting Machine with Liveness Detection Module
Volume-3 | Issue-3
An Economical Robotic Arm for Playing Chess Using Visual Servoing
Volume-2 | Issue-3
Triplet loss for Chromosome Classification
Volume-4 | Issue-1
Unstructured Noise Removal for Industrial Sensor Imaging Unit by Hybrid Adaptive Median Algorithm
Volume-3 | Issue-4
Real Time Sign Language Recognition and Speech Generation
Volume-2 | Issue-2
Analysis of Artificial Intelligence based Image Classification Techniques
Volume-2 | Issue-1
Design of ANN Based Machine Learning Method for Crop Prediction
Volume-3 | Issue-3
A REVIEW ON IOT BASED MEDICAL IMAGING TECHNOLOGY FOR HEALTHCARE APPLICATIONS
Volume-1 | Issue-1
COMPUTER VISION BASED TRAFFIC SIGN SENSING FOR SMART TRANSPORT
Volume-1 | Issue-1
Diabetic Retinopathy Detection Using Machine Learning
Volume-4 | Issue-1
Accurate Segmentation for Low Resolution Satellite images by Discriminative Generative Adversarial Network for Identifying Agriculture Fields
Volume-3 | Issue-4
Deep Learning based Handwriting Recognition with Adversarial Feature Deformation and Regularization
Volume-3 | Issue-4
State of Art Survey on Plant Leaf Disease Detection
Volume-4 | Issue-2
Optimal Compression of Remote Sensing Images Using Deep Learning during Transmission of Data
Volume-3 | Issue-4
OverFeat Network Algorithm for Fabric Defect Detection in Textile Industry
Volume-3 | Issue-4
VIRTUAL RESTORATION OF DAMAGED ARCHEOLOGICAL ARTIFACTS OBTAINED FROM EXPEDITIONS USING 3D VISUALIZATION
Volume-1 | Issue-2
Two-Stage Frame Extraction in Video Analysis for Accurate Prediction of Object Tracking by Improved Deep Learning
Volume-3 | Issue-4
Volume - 5 | Issue - 3 | september 2023
Published
31 October, 2023
The human body's largest and most defensive organ is its skin. It covers internal organs and shields the human body from extraneous substances outside of it. Numerous illnesses can harm a person's skin caused by microbes such as bacteria, fungi, and viruses; for example, MRSA (methicillin-resistant Staphylococcus aureus) infection, Herpes zoster, Acne vulgaris, warts, eczema, psoriasis, and the fifth disease. It can also be damaged by carcinogenic and tumor-inducing agents, leading to skin cancer such as melanoma, which is more fatal and life-threatening to human life. Skin diseases can be diagnosed by blood tests, tissue sample collection (biopsy), and skin examination by dermatologists and experts. If non-expert doctors or laboratory technicians examine the skin, it can lead to medical errors and misdiagnosis. A proper and precise diagnosis and detection are required to treat the specific disease. This research aims to detect dermal diseases through sample images and classify and identify the cause of disease with greater accuracy and precision in a time- and cost-efficient way. This research uses medical processing algorithms such as pre-processing and segmentation of the diseased image and image classification algorithms such as deep learning, part of a neural network, to classify the diseased medical images.
KeywordsMRSA Microbes Skin Deep Learning Skin Cancer
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