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 - 1 | Issue - 1 | september 2019
Published
September, 2019
Nowadays the skin cancer has become a more dangerous and an unpredictable disease among the humans. Nearly one million of people all over the world every year are been affected by the skin cancer and left with no treatment due to the lack of early diagnosis. Besides the usual types of cancer such as the melanoma, basal cell carcinoma and squamous cell carcinoma that could be identified easily there are certain types of unusual skin cancer such as the Merkel cell skin cancer that are rare and difficult to diagnose. As the identification of the Merkel cell skin cancer at the early stage would be very useful in deciding the necessary treatment for its cure, the paper has put forward preprocessing techniques to improve the image quality to make the further image processing procedure easy in the identification of the skin cancer. The proposed method applies the combined image enhancement and the restoration (CIEIR) on the input skin lesion images and makes it more presentable with the improved quality for the further image processing steps in the identification of the normal skin and the skin affected by the Merkel cell tumor. The CIEIR is implemented in the MATLAB and the parameters such as the PSNR, SSIM and the MSE are measured.
KeywordsSkin Cancer Merkel cell skin cancer Image Enhancement (IE) Image Restoration (IR) Fuzzy-Set Theory (FST) Integrated Wiener Filter (WF+GF (gradient filter) +MF (median filter))
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