Comparison of the histogram of oriented gradient, GLCM, and shape feature extraction methods for breast cancer classification using SVM

Breast cancer originates from the ducts or lobules of the breast and is the second leading cause of death after cervical cancer. Therefore, early breast cancer screening is required, one of which is mammography. Mammography images can be automatically identified using Computer-Aided Diagnosis by leveraging machine learning classifications. This study analyzes the Support Vector Machine (SVM) in classifying breast cancer. It compares the performance of three features extraction methods used in SVM, namely Histogram of Oriented Gradient (HOG), GLCM, and shape feature extraction. The dataset consists of 320 mammogram image data from MIAS containing 203 normal images and 117 abnormal images. Each extraction method used three kernels, namely Linear, Gaussian, and Polynomial. The shape feature extraction-SVM using Linear kernel shows the best performance with an accuracy of 98.44 %, sensitivity of 100 %, and specificity of 97.50 %.

Detection of Architectural Distorted Region in Mammogram Images Using Hybrid Classification Approaches

The architectural distorted regions in mammogram images are detected and segmented using computer aided hybrid classification approach in this paper. The main importance of this research work is to provide a computer aided methodology for screening the distorted regions in mammogram images. In present approach, the classification accuracy of the conventional methods is not suitable for further diagnosis process such as malignant and benign. Hence, the main objective of this paper is to develop an efficient architectural region detection method using soft computing method with high classification accuracy for further diagnosis purpose. This proposed method has two stages of the proposed flow as architectural distorted detected mammogram image and segmentation of architectural distorted regions in mammogram images. The first stage of this proposed method uses Random Forest (RF) classification method which classifies the source mammogram image into either normal or abnormal. In second stage of the proposed method, the abnormal image is further classified into either Benign or Malignant using Adaptive Neuro Fuzzy Inference System (ANFIS) classification approach. The proposed methodology for architectural distorted region detection is tested on the publicly available mammogram datasets Mammographic Image Analysis Society (MIAS) and Digital Database for Screening Mammography (DDSM) respectively. In this paper, the mammogram images from MIAS dataset are grouped into normal case (156 images), benign case (122 images) and malignant case (98 images). The mammogram images from DDSM dataset are grouped into normal case (144 images), benign case (112 images) and malignant case (145 images). The overall average detection rate of the proposed system on the mammogram images in MIAS dataset is about 98.7%. The overall average detection rate of the proposed system on the mammogram images in DDSM dataset is about 98.3%. The extensive simulations are carried out on the mammogram images which are obtained from these dataset and the results are compared with stated of art methods.

Chaotic Duck Traveler Optimization (cDTO) Algorithm for Feature Selection in Breast Cancer Dataset Problem

Objective: 1 of every 3 individuals will be determined to have malignancy in the course of their life. Currently, there are more than 3.8 million ladies who have been determined to have breast malignancy in the United States. 2021 is practically around the bend, yet there's still an ideal opportunity to help ladies confronting breast malignancy in 2020. In this paper, chaotic based duck travel optimization (cDTO) meta-heuristic algorithm is introduced to classifying the input images from Mammogram Image Analysis Society (MIAS) database. Methods: Linear Discriminant Analysis is used to extract the mammogram image features. (cDTO-LDA) is an intrinsic algorithm to remove irrelevant features and select the optimal features by using wavelet families Haar (harr), db4 (daubechies), bior4.4 (Biorthogonal), Symlets (SYM8), “Discrete” FIR approximation of Meyer wavelet (dmey) features. Results: These selected features are evaluated by the quality measures such as accuracy, sensitivity, specificity, error rate that are clearly shows the high exactness of cDTO classifier is 98.5%. CSA-LDA classifier has the minimum exactness. Conclusion: Algorithm efficiency is proved by the promising results achieved by the proposed algorithm for selecting the best feature of breast cancer classification.

Automated Assistance for Breast Cancer Identification on Mammograms Using Computer Vision Algorithms

One of the greatest health problems in the world is breast cancer. If these breast cancer abnormalities are identified early, there is a maximum chance of recovery. We can go for this early prediction. It is one of the most effective detection and screening strategies and is widely used. The basic goal of CAD systems is to support physicians in the process of diagnosis. CAD systems, however, are very expensive. Our emphasis is on developing a CAD system that is low-cost and effective. To categorize breast cancer as either benign or malignant, a computer-aided detection approach is suggested. The standard mammogram image corpus, Digital Database used for Screening Mammography, images are used for enhancement, segmented and GLCM, intensity and histogram methods are used to extract features. The work is carried out by effective multilayer perceptron classifier (MLP) and support vector machine (SVM). Compare the performance of the classifiers. The proposed approach achieved 96 % accuracy and 8% improvement in accuracy compared to previous approaches with same dataset [4].