Abstract: Diagnosis of breast cancer at early stages has seen better clinical and survival outcomes. Survival rates in developing countries like India are very low due to accessibility and affordability issues of screening tests such as Mammography. In addition, Mammography is not much effective in younger women with dense breasts. This leaves a gap in current screening methods. Thermalytix is a new technique for detecting breast abnormality in a non-contact, non-invasive way. It is an AI-enabled computer-aided diagnosis solution that automates interpretation of high resolution thermal images and identifies potential malignant lesions. The solution is low cost, easy to use, portable and is effective in all age groups. This paper presents the results of a retrospective comparative analysis of Thermalytix over Mammography and Clinical Breast Examination for breast cancer screening. Thermalytix was found to have better sensitivity than both the tests, with good specificity as well. In addition, Thermalytix identified all malignant patients without palpable lumps.
Abstract: Early detection of breast cancer saves many thousands
of lives each year via application of mammography and genetic
screening and many more lives could be saved if nurses are involved
in breast care screening practices. So, the aim of the study was to
identify nurse's role in early detection of breast cancer through
mammography and genetic screening and its impact on patient's
outcome. In order to achieve this aim, 400 women above 40 years,
asymptomatic were recruited for mammography and genetic
screening. In addition, 50 nurses and 6 technologists were involved in
the study. A descriptive analytical design was used. Five tools were
utilized: sociodemographic, mammographic examination and risk
factors, women's before, during and after mammography, items
relaying to technologists, and items related to nurses were also
obtained. The study finding revealed that 3% of women detected for
malignancy and 7.25% for fibroadenoma. Statistically significant
differences were found between mammography results and age,
family history, genetic screening, exposure to smoke, and using
contraceptive pills. Nurses have insufficient knowledge about
screening tests. Based on these findings the present study
recommended involvement of nurses in breast care which is very
important to in force population about screening practices.
Abstract: Clusters of Microcalcifications (MCCs) are most frequent symptoms of Ductal Carcinoma in Situ (DCIS) recognized by mammography. Least-Square Support Vector Machine (LS-SVM) is a variant of the standard SVM. In the paper, LS-SVM is proposed as a classifier for classifying MCCs as benign or malignant based on relevant extracted features from enhanced mammogram. To establish the credibility of LS-SVM classifier for classifying MCCs, a comparative evaluation of the relative performance of LS-SVM classifier for different kernel functions is made. For comparative evaluation, confusion matrix and ROC analysis are used. Experiments are performed on data extracted from mammogram images of DDSM database. A total of 380 suspicious areas are collected, which contain 235 malignant and 145 benign samples, from mammogram images of DDSM database. A set of 50 features is calculated for each suspicious area. After this, an optimal subset of 23 most suitable features is selected from 50 features by Particle Swarm Optimization (PSO). The results of proposed study are quite promising.