Online ISSN: 2515-8260

Keywords : Mammography


DETECTION OF MICROCALCIFICATION CLUSTERS USING STATISTICAL PARAMETERS AND DYADIC CONTOURLET TRANSFORM BASED PRECISION ENHANCEMENT

Venmathi A R; A. Senthil Kumar; M. Gomati; G. Suresh

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 11, Pages 2423-2436

Recent scenario, breast cancer found to be a threat and dangerous carcinoma among women in the world. In contemplation of reducing the breast, cancer-related death needs an efficient computer-aided diagnosis (CAD) system. The discrimination of microcalcification clusters (MCCs) is an important manifestation for the early diagnosis of breast cancer. This paper focuses on the detection of breast cancers cells size below 2mm. To achieve précised enhanced cancer cell region an efficient technique dyadic Counterlet transform (DCTs) in two dimension is proposed. The enhancement of cancer cell region obtained through preserving the boundaries and borders with curvature for a small region.

DETECTION OF MICROCALCIFICATION CLUSTERS USING STATISTICAL PARAMETERS AND DYADIC CONTOURLET TRANSFORM BASED PRECISION ENHANCEMENT

Venmathi A R; A. Senthil Kuma; M. Gomati; G. Suresh

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 9, Pages 2423-2436

—Recent scenario, breast cancer found to be a threat and dangerous carcinoma among women in the world. In contemplation of reducing the breast, cancer-related death needs an efficient computer-aided diagnosis (CAD) system. The discrimination of microcalcification clusters (MCCs) is an important manifestation for the early diagnosis of breast cancer. This paper focuses on the detection of breast cancers cells size below 2mm. To achieve précised enhanced cancer cell region an efficient technique dyadic Counterlet transform (DCTs) in two dimension is proposed. The enhancement of cancer cell region obtained through preserving the boundaries and borders with curvature for a small region.

A Study Of Breast Cancer Analysis Using K-Nearest Neighbor With Different Distance Measures And Classification Rules Using Machine Learning.

M.D. Bakthavachalam; Dr.S .Albert Antony Raj

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 3, Pages 4842-4851

Breast Cancer is one of the life threatening disease among females all over the world. This killer disease however when it can be detected in its early stages can be a life saver for many. Radiologists uses the mammography images to detect the presence and absence of Breast Cancer. The field of Bio-informatics leverages the Machine learning techniques for diagnosis of Breast cancer in particular. This research work experiments with the two most popularly used Supervised Machine Learning Algorithms, K-Nearest Neighbour and Naive Bayes. This work predicts Breast Cancer on the The Breast Cancer Data Set (BCD) taken from the UCI Machine Learning Repository. A comparative analysis between the two approaches are made in terms of its performance metrics using CV techniques. The proposed work has achieved a best accuracy of 97.15% by employing the KNN algorithm and a lowest error rate of 96.19% using NB classifier.

Detection of Microcalcifications in Digital Mammogram Using Curvelet Fractal Texture Features

Dr. Vimal Kumar M N; Divya M; Ilakkia M M; Jayanthi S; Dr Gomathi V

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 2, Pages 251-256

In this work, an attempt is made to Segment and find features of the segmented
mammogram Images and finally mammogram images are classified as normal and
abnormal. The mammogram images used for this work are considered from MIAS
Database. The database includes 322 digitized films and all the images are of size
1024x1024. It consists of 322 images (208 normal images and 114 abnormal images).
Initially, mammogram images are subjected to pre-processing using Discrete Cosine
Transform to enhance the edges of the mammograms. Then, sharpened images are cl
using the Fuzzy C-means Clustering algorithm. After segmentation, Curvelet coefficient
and fractal Dimension values are obtained using Discrete Curvelet Transformand Fractal
textures respectively. The average values of obtained curvelet coefficient and fractal
dimension values for both normal and abnormal mammogram images are compared.
Finally, The mammogram images are classified using an Ensemble Fully Complex-
Valued Relaxation Network Classifier. The Classifier is used foe the classification of the
mammogram images as normal and abnormal.