European Journal of Molecular & Clinical Medicine

The development of breast cancer in many women appears to be related to female reproductive hormones, particularly endogenous estrogens. Early age at menarche, nulliparity or late age at first full-term pregnancy, and late age at menopause increase the risk of developing breast cancer. In postmenopausal women, obesity and postmenopausal hormone replacement therapy (HRT), both of which are positively correlated with plasma estrogen levels and plasma estradiol levels, are associated with increased breast cancer risk. Most hormonal risk factors have a relative risk (RR) of ≤2 for breast cancer development. The elective Modified Radical Mastectomy procedure was done in standard fashion. Patients in group A (Study group) received intraoperatively instillation of 0.5% bupivacaine into operative bed at the end of surgery. Patients in group B (Placebo group) received intraoperative instillation of normal saline into the operative bed at the end of surgery position. Approval from the ethical committee of the institution was obtained. All the patients were explained about the basis of the study and informed consent were obtained. Patients who received bupivacaine had longer postoperative analgesia when compared with normal saline group.

Background: Among women, incidence rates for breast cancer significantly exceeded
those for other cancers in both transitioned and transitioning countries, it remaining as
a remaining as most commonly diagnosed cancer and the prominent cause of cancer
death in women worldwide. The present study was conducted to assess Clinico-radiopathological
Features and Biological Behavior of Breast Cancer in Young Indian
Women.
Materials and Methods: A prospective descriptive study was done among women aged
less than 40 years diagnosed with breast cancer. In patients with suspicious clinical or
ultrasound findings or if biopsy yielded malignancy, digital mammography was
performed. All the BI-RADS 4 and 5 lesions and few of the BI-RADS 3 lesions were
biopsied, and samples were sent for histopathology (HPE) and immunohistochemistry
(IHC) examinations. The data was collected on Microsoft Office Excel 2013 and
statistical analysis was performed using IBM SPSS version 21(Illinois, Chicago). P value
<0.05 was considered significant.

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.

—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.

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.

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.