Image processing and machine learning techniques used in computer-aided detection system for mammogram screening - a review

This paper aims to review the previously developed Computer-aided detection (CAD) systems for mammogram screening because increasing death rate in women due to breast cancer is a global medical issue and it can be controlled only by early detection with regular screening. Till now mammography is the widely used breast imaging modality. CAD systems have been adopted by the radiologists to increase the accuracy of the breast cancer diagnosis by avoiding human errors and experience related issues. This study reveals that in spite of the higher accuracy obtained by the earlier proposed CAD systems for breast cancer diagnosis, they are not fully automated. Moreover, the false-positive mammogram screening cases are high in number and over-diagnosis of breast cancer exposes a patient towards harmful overtreatment for which a huge amount of money is being wasted. In addition, it is also reported that the mammogram screening result with and without CAD systems does not have noticeable difference, whereas the undetected cancer cases by CAD system are increasing. Thus, future research is required to improve the performance of CAD system for mammogram screening and make it completely automated

A Computational Approach to Predict the Severity of Breast Cancer through Machine Learning Algorithms

Breast cancer is one type of cancer which causes from breast tissue. A lump in the breast, skin dimpling, breast shape changes, fluid from the nipple, or a red scaly patch of skin are some of signs of breast cancer. In the world, cancer is one of the most leading causes of deaths among the women. Among the cancer diseases, breast cancer is especially a concern in women. Mammography is one of the methods for finding tumor in the breast. This method is utilized to detect the cancer which is helpful for the doctor or radiologists. Due to the inexperience�s in the field of cancer detection, the abnormality is missed by doctor or Radiologists. Segmentation is very expensive for doctor and radiologists to examine the data in the mammogram. In mammogram the accuracy rate is based on the image segmentation. The recent clustering techniques are presented in this paper for detection of breast cancer. These Classification algorithms have been mostly studied which is applied in a various application areas. To maximize the efficiency of the searching process various clustering techniques are recommended. In this paper, we have presented a survey of Classification techniques

Microcalcifications Detection Using Image And Signal Processing Techniques For Early Detection Of Breast Cancer

Breast cancer has transformed into a severe health problem around the world. Early diagnosis is an important factor to survive this disease. The earliest detection signs of potential breast cancer that is distinguishable by current screening techniques are the presence of microcalcifications (MCs). MCs are small crystals of calcium apatite and their normal size ranges from 0.1mm to 0.5mm single crystals to groups up to a few centimeters in diameter. They are the first indication of breast cancer in more than 40% of all breast cancer cases, making their diagnosis critical. This dissertation proposes several segmentation techniques for detecting and isolating point microcalcifications: Otsu’s Method, Balanced Histogram Thresholding, Iterative Method, Maximum Entropy, Moment Preserving, and Genetic Algorithm. These methods were applied to medical images to detect microcalcifications. In this dissertation, results from the application of these techniques are presented and their efficiency for early detection of breast cancer is explained. This dissertation also explains theories and algorithms related to these techniques that can be used for breast cancer detection

Breast cancer diagnosis: a survey of pre-processing, segmentation, feature extraction and classification

Machine learning methods have been an interesting method in the field of medical for many years, and they have achieved successful results in various fields of medical science. This paper examines the effects of using machine learning algorithms in the diagnosis and classification of breast cancer from mammography imaging data. Cancer diagnosis is the identification of images as cancer or non-cancer, and this involves image preprocessing, feature extraction, classification, and performance analysis. This article studied 93 different references mentioned in the previous years in the field of processing and tries to find an effective way to diagnose and classify breast cancer. Based on the results of this research, it can be concluded that most of today’s successful methods focus on the use of deep learning methods. Finding a new method requires an overview of existing methods in the field of deep learning methods in order to make a comparison and case study

Breast cancer diagnosis system using hybrid support vector machine-artificial neural network

Breast cancer is the second most common cancer occurring in women. Early detection through mammogram screening can save more women’s lives. However, even senior radiologists may over-diagnose the clinical condition. Machine learning (ML) is the most used technique in the diagnosis of cancer to help reduce human errors. This study is aimed to develop a computer-aided detection (CAD) system using ML for classification purposes. In this work, 80 digital mammograms of normal breasts, 40 of benign and 40 of malignant cases were chosen from the mini MIAS dataset. These images were denoised using median filter after they were segmented to obtain a region of interest (ROI) and enhanced using histogram equalization. This work compared the performance of artificial neural network (ANN), support vector machine (SVM), reduced features of SVM and the hybrid SVM-ANN for classification process using the statistical and gray level co-occurrence matrix (GLCM) features extracted from the enhanced images. It is found that the hybrid SVM-ANN gives the best accuracy of 99.4% and 100% in differentiating normal from abnormal, and benign from malignant cases, respectively. This hybrid SVM-ANN model was deployed in developing the CAD system which showed relatively good accuracy of 98%

Machine Learning Approaches to Understanding and Predicting Cancer Screening Follow Through with Population and Health System Data

Introduction Cancer is the second leading cause of death in the United States and cancer screening is a primary tool to reduce mortality. However, not all who are recommended to be screened actually follow through. This study investigates whether electronic medical record and geographic data is suitable to predict which patients are at risk of missing recommended screenings. The goal of this investigation is to design a data informed system that can automate the prediction of those at risk for missing screenings and provide insights into underlying reasons. This will enable resources to be focused to increase cancer screening adherence, with the overall goal of reducing mortality from cancer. Methods Data for this study was sourced from de-identified electronic medical records from the Medical University of South Carolina’s patient population and publicly available geographic datasets. This data was used to train a series of machine learning models to predict which patients would follow through with cancer screening tests, and describe underlying associations to diagnoses data, cancer histories and social determinants of health. Results This study found that it was possible to systematically identify small groups of female patients that are unlikely to follow through with mammogram screening. However, similar results were not found predicting lung cancer screening follow-though. Additionally, patterns associating social determinants at the county level cannot be used to make accurate predictions about individual patient follow through. It was also demonstrated that the core relationship between screening and mortality does not hold in high proportion minority areas. Conclusion This study successfully shows that an automated system for identifying small groups of patients unlikely to complete mammogram screening is achievable and sets forth a methodology to development. It also provides valuable insights into the nature of social determinants associated with patients and their limits when geographically attributed