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 - 2 | june 2023
Published
30 June, 2023
Monkeypox is an infectious zoonotic disease with clinical features similar to those actually observed in victims with smallpox, however being medically less severe. With the control of infectious smallpox diseases in 1980 as well as the termination of smallpox by immunization, monkeypox has become the most significant orthopoxvirus affecting global health. It is very important to prevent and diagnose this disease immediately and efficiently before its spread worldwide. Currently, the traditional system is used for the diagnosis of this infectious disease, in which a medical practitioner identifies monkeypox disease with swabs of fluid from skin rash. This approach has a lot of limitations such as it requires medical expertise, is costly and slow, and its result is not satisfactory. AI-based technologies may assist prevent and identify this infectious disorder. Because of the limitations, this proposed work suggests an AI-based diagnosis system which can detect monkeypox virus efficiently and immediately. Five transfer learning models are applied on image -based dataset with some pre-processing and optimization techniques for monkeypox virus detection. The Inception-Resnet outperformed by achieving 97% accuracy, VGG16 achieved 94% accuracy, Inception achieved 96% accuracy, VGG19 achieved 91% accuracy, and Resnet50 achieved 71% accuracy. The positive results of this investigation suggest that this strategy outperforms the current approaches. The dataset used in this proposed work is obtained from Kaggle online repository and some new patients’ data are added from various sources. This suggested strategy can be used by health professionals for screening.
KeywordsMonkeypox disease Transfer learning pre-trained models classification
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