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 Novel Segmentation Approach Combining Region- and Edge-Based Information for Ultrasound Images

Ultrasound imaging has become one of the most popular medical imaging modalities with numerous diagnostic applications. However, ultrasound (US) image segmentation, which is the essential process for further analysis, is a challenging task due to the poor image quality

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

Logic programming and artificial neural networks in breast cancer detection

About 90% of breast cancers do not cause or are capable of producing death if detected at an early stage and treated properly. Indeed, it is still not known a specific cause for the illness. It may be not only a beginning, but also a set of associations that will determine the onset of the disease. Undeniably, there are some factors that seem to be associated with the boosted risk of the malady. Pondering the present study, different breast cancer risk assessment models where considered. It is our intention to develop a hybrid decision support system under a formal framework based on Logic Programming for knowledge representation and reasoning, complemented with an approach to computing centered on Artificial Neural Networks, to evaluate the risk of developing breast cancer and the respective Degree-of-Confidence that one has on such a happening.This work has been supported by FCT – Fundação para a Ciência e Tecnologia within the Project Scope UID/CEC/00319/2013

Advancement in Research Techniques on Medical Imaging Processing for Breast Cancer Detection

With the advancement of medical image processing, the area of the healthcare sector has started receiving the benefits of the modern arena of diagnostic tools to identify the diseases effectively. Cancer is one of the dreaded diseases, where success factor of treatment offered by medical sector is still an unsolved problem. Hence, the success factor of the treatment lies in early stage of the disease or timely detection of the disease. This paper discusses about the advancement being made in the medical image processing towards an effective diagnosis of the breast cancer from the mammogram image in radiology. There has been enough research activity with various sorts of advances techniques being implemented in the past decade. The prime contribution of this manuscript is to showcase the advancement of the technology along with illustration of the effectiveness of the existing literatures with respect to research gap

The Combination of Mammography and MRI for Diagnosing Breast Cancer Using Fuzzy NN and SVM

Breast cancer is one of the common cancers among women so that early diagnosing of it can effectively help its treatment in this study, considering combination of Mammography and MRI pictures, we will try to recognize glands in existing pictures which identify all around of gland complete and precisely and separate it completely. In this method using artificial intelligence algorithm such as Affine transformation, Gabor filter, neural network, and support vector machine, image analysis will be carried out. The accuracy of proposed method is 98.14. In this work a special framework is presented which simplifies cancer diagnosis. The algorithm of proposed method is tested on z16 images. High speed and lack of human error are the most important factors in proposed intelligent system

AN AUTOMATED COMPUTER-AIDED DETECTION (CADe) AND DIAGNOSIS (CADx) SYSTEM FOR BREAST MICROCALCIFICATIONS IN MAMMOGRAMS

ABSTRACTAn automated computer aided diagnosis system has been proposed for detection of microcalcification (MC) clusters in mammograms. The proposed system is a whole system including suspicious regions identification, MCs detection, false positive reduction and benign/malign classification. For classification of suspicious microcalcification regions, a multilayer perceptron (MLP) neural network was used with grey level co-occurrence matrix (GLCM) and statistical features.  Then to decrease the false positive classification ratio, we used cascade correlation neural network (CCNN) with grey level run length matrix (GLRLM) features. In the last step, hybrid form of discriminant analysis and support vector machine (SVM) methods were used with GLRLM features for benign/malign classification of detected MC clusters. The open access Mammographic Image Analysis Society (MIAS) database was used for the study. Experimental results show that the proposed algorithm obtained 86% sensitivity, 98.3% specificity and 1.163 FPpI rates for detection an for diagnosis of breast cancer, the obtained sensitivity and specificity values are 100% and 100% respectively. Despite the vision difficulty of MC clusters, the novel system provides very satisfactory results. Furthermore, the developed system is fully automatic whole system which gives outputs as percentages and transformed assessment categories. Keywords: Mammograms, Breast cancer, Computer aided diagnosis, Cascade correlation neural network (CCNN), Grey level co-occurrence matrix (GLCM), Grey level run length matrix (GLRLM). 

BREAST CANCER DIAGNOSIS USING WRAPPER-BASED FEATURE SELECTION AND ARTIFICIAL NEURAL NETWORK

Breast cancer is commonest type of cancers among women. Early diagnosis plays a significant role in reducing the fatality rate. The main objective of this study is to propose an efficient approach to classify breast cancer tumor into either benign or malignant based on digitized image of a fine needle aspirate (FNA) of a breast mass represented by the Wisconsin Breast Cancer Dataset. Two wrapper-based feature selection methods, namely, sequential forward selection(SFS) and sequential backward selection (SBS) are used to identify the most discriminant features which can contribute to improve the classification performance. The feed forward neural network (FFNN) is used as a classification algorithm. The learning algorithm hyper-parameters are optimized using the grid search process. After selecting the optimal classification model, the data is divided into training set and testing set and the performance was evaluated. The feature space is reduced from nine feature to seven and six features using SFS and SBS respectively. The highest classification accuracy recorded was 99.03% with FFNN using the seven SFS selected features. While accuracy recorded with the six SBS selected features was 98.54%. The obtained results indicate that the proposed approach is effective in terms of feature space reduction leading to better accuracy and efficient classification model

Implementing decision tree-based algorithms in medical diagnostic decision support systems

As a branch of healthcare, medical diagnosis can be defined as finding the disease based on the signs and symptoms of the patient. To this end, the required information is gathered from different sources like physical examination, medical history and general information of the patient. Development of smart classification models for medical diagnosis is of great interest amongst the researchers. This is mainly owing to the fact that the machine learning and data mining algorithms are capable of detecting the hidden trends between features of a database. Hence, classifying the medical datasets using smart techniques paves the way to design more efficient medical diagnostic decision support systems. Several databases have been provided in the literature to investigate different aspects of diseases. As an alternative to the available diagnosis tools/methods, this research involves machine learning algorithms called Classification and Regression Tree (CART), Random Forest (RF) and Extremely Randomized Trees or Extra Trees (ET) for the development of classification models that can be implemented in computer-aided diagnosis systems. As a decision tree (DT), CART is fast to create, and it applies to both the quantitative and qualitative data. For classification problems, RF and ET employ a number of weak learners like CART to develop models for classification tasks. We employed Wisconsin Breast Cancer Database (WBCD), Z-Alizadeh Sani dataset for coronary artery disease (CAD) and the databanks gathered in Ghaem Hospital’s dermatology clinic for the response of patients having common and/or plantar warts to the cryotherapy and/or immunotherapy methods. To classify the breast cancer type based on the WBCD, the RF and ET methods were employed. It was found that the developed RF and ET models forecast the WBCD type with 100% accuracy in all cases. To choose the proper treatment approach for warts as well as the CAD diagnosis, the CART methodology was employed. The findings of the error analysis revealed that the proposed CART models for the applications of interest attain the highest precision and no literature model can rival it. The outcome of this study supports the idea that methods like CART, RF and ET not only improve the diagnosis precision, but also reduce the time and expense needed to reach a diagnosis. However, since these strategies are highly sensitive to the quality and quantity of the introduced data, more extensive databases with a greater number of independent parameters might be required for further practical implications of the developed models