This study intends to throw some light on the different treatment gateways of breast cancer.
As we know that women are worst affected by this life threatening disease around the globe,
everyone should be aware of the fact that this disease can be tackled if it is detected at the
initial stage. In India, the most number of women are affected by this fatal carcinoma and that
results in a huge death rate. MRI, Biopsy, USG, Mammography, Histopathological images
and many other diagnostic tests can confirm the presence of breast cancer in women. This
paper will focus on the prediction of the test samples to be malignant or not by studying the
ways of performing machine learning based computer aided systems. By reviewing many
important and promising papers in this area, it has been found that there is an established
system of detection of carcinoma that is known as Computer Aided Detection. This system
consists of the different stages as in image pre-processing, segmentation of images, extraction
of relevant features and image classification. We also found from the review that the efficiency of CAD systems increases when the methodologies like CART, Decision Tree
Classifier (DT), Logistic Regression (LR), Naïve Bayes (NB), Ensemble, Random Forest
Classifier (RF), and K-nearest neighbor classifiers (KNN) used to extracted features. We
reviewed several research papers and found a plethora of methodologies available for early
detection of breast cancer by using CAD. When the WBCD dataset was evaluated by using
Ensemble technique, it recorded about 98% of accuracy. Previously, radiologists could not
diagnose breast cancer with so much efficacy as there was a scarcity of so many efficient
techniques which are available nowadays. Although the ultimate result of the tests depends
on the diagnostic ability of the radiologists, they get a significant amount of assistance by the
latest methodologies.
Keywords: MRI, Biopsy, CAD, Histopathology, Invasive Ductal Carcinoma, Machine
Learning, Deep Learning
Publication date: 01/11/2021
https://ijbpas.com/pdf/2021/November/MS_IJBPAS_2021_NOV_SPCL10691.pdf
Download PDF
https://doi.org/10.31032/IJBPAS/2021/10.11.1069
