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
28 October, 2023
Among all cancer types, breast cancer is the most prevalent. For females, it ranks as the second most common cause of cancer-related mortality. Every 1 person per 28 people in lifetime have a chance of developing breast cancer, according to statistics. Each year, it is estimated that over two million women encounter it. The high-risk group in India has an average age of 43–46 years, but in the west, women between the ages of 53 and 57 are more likely to get breast cancer. While there is no known cure for breast cancer, early detection and diagnosis significantly improves chances of survival. Treatment for breast cancer patients may be possible if the disease is identified and diagnosed early. Diagnosing benign from malignant tumours and determining whether a breast cancer case is early or progressed presents a number of challenges for cancer researchers. This study compares many methods for detecting breast cancer and provides a detailed analysis of each, highlighting the methods that are most accurate and economical. This study's primary goal is to comprehend the fundamental principles behind the various technologies used in breast cancer diagnosis. It is simple to save lives by spreading awareness of the latest and most varied detection and screening techniques.
KeywordsBreast Cancer detection mammography Machine Learning Image processing Mortality Signal Processing Artificial Intelligence
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