Classification of Mammograms using Various Feature Extraction Methods and Machine Learning

Abstract

 Abstract: Breast cancer is an alarming disease which takes millions of lives every year. In 2018, it was anticipated that 627,000 women died due to breast cancerwhich is around 15% of all deaths caused due to different types of cancers among women. Currently, risk factors of breast cancer cannot be avoided, and early detection is the only way of survival. Automated detection of breast cancer with the help of image processing methods and machine learning algorithms helps in giving more accurate results and less human power. In the proposed system, multiple features are extracted using HSV histogram, LBP, GLCM, 2-D DWT. Support vector machine and LIBSVM classifiers are used for the classification of mammogram images if it's benign or malign in nature. For classification, the INbreast dataset have been used which includes 115 cases containing 410 images. The dataset is divided into benign and malign category based upon BI-RAIDS scale. According to this partition we have 243 benign images and 100 malign images present in this dataset and a feature matrix of 595 features in total is generated for balanced and unbalanced datasets respectively and fed into SVM and LIBSVM to distinguish the data. The balanced datasets on LIBSVM gave best results with 92% accuracy, 84% sensitivity, 100% specificity and 91.30% F1 score followed by SVM which gave 75% accuracy, 73.61% sensitivity, 76.66% specificity and 75.8% F1 score.

A Review of Brain Tumor Diagnosis and Segmentation

“…When the RF energy pulse is stopped, the water molecules return to their state of equilibrium and line up with the magnetic field once more [16]. This causes the water molecules to produce RF energy, which the scanner detects and transforms into visual images [17]. The tissue structure determines what amount of RF energy can be given off by the water molecules.…”

“…On T1 and T2 images of a tumor brain, the intensity level of the tumorous tissues differs. [17] Most tumors show low or medium gray intensity on T1-w. On T2-w, the majority of tumors exhibit bright intensity [18]. Examples of MRI tumor intensity level includes are shown in Figure 4.…”