No abstract

Mammograms have been acknowledged as one of the most reliable screening tools as well as a key diagnostic mechanism for early breast cancer detection. Though mammography is a valuable screening tool for detecting malignant growth in breasts, its competence as a diagnostic tool is heavily reliant on the radiologists’ understanding. Automated systems are now widely used for detection of breast cancer. Image processing techniques were widely used in automated systems for classifying mammograms. Of late with the advent of deep learning (DL) where images can be processed directly for classification, the DL is widely researched for medical image classification. Basically, DL techniques are representation-learning methods which aid in understanding data like sounds, images as well as texts. DL algorithms have the ability to learn multiple levels of representation as well as abstraction. Residual network (ResNet) is given due consideration as a kind of highly advanced Convolutional Neural Networks (CNNs). This work has offered a potential application of Visual Geometry Group (VGG), Residual network (ResNet) and Inception based CNN model for differentiating the mammograms into the abnormal class and the normal class. Experimental results demonstrated that the deep learners are effective for classifying mammograms and Inception deep learner achieved the best accuracy of 91.49%.

This paper presents a novel approach for effective matching of similar shapes from skeleton and boundary features. The features identified from the shape are the junction points, end points, and maximum length from single pixel pruned skeleton of the shape. Another two features identified from the boundary are junctions and boundary length of the shape. These five features are then used for shape matching. We tested these features on Kimia shapes dataset and tools dataset. The matching process from these features has produced good results, showing the probable of the developed method in a variety of computer vision and pattern recognition domains. The results demonstrate these features are rotational and translation invariant.