Intelligent Computer-aided Diagnosis System Research Articles

An Intelligent Computer Aided Diagnosis System for Classification of Ovarian Masses using Machine Learning Approach

Ovarian cancer, a difficult and often asymptomatic malignancy, remains a substantial global health concern in women. An ovary is a female reproductive organ, which lies on each side of the uterus and used to store eggs. Computer-aided diagnosis (CAD) is an approach that involves using computer algorithms and machine learning techniques to assist medical professionals in diagnosing ovarian malignancies, benign tumors or Poly-cystic ovaries (PCOS). The need for models that can effectively predict benign ovarian tumors and ovarian cancer has led to the use of machine learning techniques. Our research objective is to propose a machine learning-based system for accurate and early ovarian mass detection utilizing novel annotated ovarian masses. We have used an actual patient database whose input features were extracted from 187 transvaginal ultrasound images from database. The input image is preprocessed using the Block Matching 3D filter. The process involves employing binary and watershed segmentation techniques, followed by the integration of Gabor, Gray-Level Co-Occurrence Matrix (GLCM), Tamura, and edge feature extraction methods. K-Nearest Neighbors (KNN) and Random Forest (RF) are two classifiers used for classification. Based on our results, we are able to demonstrate that binary segmentation with RF classifiers is more accurate (above 86%) than KNN classifiers (under 84%).

Explanation models as a component of the intelligent computer-aided diagnosis systems in medicine: a brief review

The paper considers the most important and effective approaches and models for explaining and interpreting diagnostic results obtained using intelligent computer-aided diagnosis systems. The need to use them is due to the fact that the intelligent computer-aided diagnosis system itself is a “black box” and it is important for the doctor not only to get the patient’s diagnosis, but also to understand why such a diagnosis is stated, what elements of the patient information are the most significant from the point of view of the diagnosis. Reviews of the main approaches to explain predictions of machine learning models applied to general areas as well as to medicine are presented. It is shown how different types of the initial patient information impact on the choice of explanation models. Models are considered when visual or tabular information is available. Example-based explanation models are also studied. The purpose of the work is to review the main explanation models and their dependence on types of information about the patient.

Intelligent computer aided diagnosis system to enhance mass lesions in digitized mammogram images

<span>The paper presents an intelligent system to enhance mass lesions in digitized mammogram images. This system can assist radiologists in detecting mass lesions in mammogram images as an early diagnosis of breast cancer. In this paper, the early detection of mass lesion is visually detected by enhancing mass lesions in mammogram images using hybrid neuro-fuzzy technique. Fuzzified engine is proposed as a first step to convert all pixels in mammogram image to a fuzzy value using three linguistic labels. After that, artificial neural networks are used instead of the inference engine to accurately detect the mass lesions in the mammogram images in a short time. Finally, five linguistic labels are used as a defuzzifier engine to restore the mammogram image. Processed mammogram images are extensively evaluated using two different types of mammogram resources, mammographic image analysis society (MIAS) and University of South Florida (USF) databases. The results show that the proposed intelligent computer aided diagnosis system can successfully enhance the mass lesions in mammogram images with minimum number of false positive regions.</span>

Intelligent Computer-Aided Prostate Cancer Diagnosis Systems: State-of-the-Art and Future Directions

Prostate Cancer (PCa) is one of the common cancers among men in the world. About 16.67% of men will be affected by PCa in their life. Due to the integration of magnetic resonance imaging in the current clinical procedure for detecting prostate cancer and the apparent success of imaging techniques in the estimation of PCa volume in the gland, we provide a more detailed review of methodologies that use specific parameters for prostate tissue representation. After collecting over 200 researches on image-based systems for diagnosing prostate cancer, in this paper, we provide a detailed review of existing computer-aided diagnosis (CAD) methods and approaches to identify prostate cancer from images generated using Near-Infrared (NIR), Mid-Infrared (MIR), and Magnetic Resonance Imaging (MRI) techniques. Furthermore, we introduce two research methodologies to build intelligent CAD systems. The first methodology applies a fuzzy integral method to maintain the diversity and capacity of different classifiers aggregation to detect PCa tumor from NIR and MIR images. The second methodology investigates a typical workflow for developing an automated prostate cancer diagnosis using MRI images. Essentially, CAD development remains a helpful tool of radiology for diagnosing prostate cancer disease. Nonetheless, a complete implementation of effective and intelligent methods is still required for the PCa-diagnostic system. While some CAD applications work well, some limitations need to be solved for automated clinical PCa diagnostic. It is anticipated that more advances should be made in computational image analysis and computer-assisted approaches to satisfy clinical needs shortly in the coming years.

An intelligent computer-aided diagnosis approach for atrial fibrillation detection based on multi-scale convolution kernel and Squeeze-and-Excitation network

Atrial fibrillation (AF) is a most common arrhythmia with high morbidity and mortality. However, the conventional detection of AF is time-consuming and laborious because it is mainly completed by physician's visual inspection of electrocardiogram (ECG). Thus, it is essential to build the intelligent computer-aided diagnosis system strategy for AF detection. In this work, we present a novel intelligent approach based on the multi-scale convolution kernel (MCK) and Squeeze-and-Excitation network (SENet) for AF detection. The model not only is able to overcome the limitations that exist in the single-scale convolution kernel of traditional convolution neural network (CNN), but also explicitly establish the inter-dependences between the extracted feature channels and screen out the critical ECG features for AF signals recognition, thus improving the model performance. The results demonstrate that the proposed model achieves noticeable performance improvements with the accuracy of 98.3% and 97.5% using a subject-independent validation scheme on the two public databases. Besides, the corresponding ablation experiments show the effectiveness of the proposed MCK strategy. To our knowledge, this is the first time to redesign the convolution kernel in traditional CNN for AF detection, while showing its great potential as an auxiliary tool to help physicians.

AI-Enhanced Diagnosis of Challenging Lesions in Breast MRI: A Methodology and Application Primer.

Computer‐aided diagnosis (CAD) systems have become an important tool in the assessment of breast tumors with magnetic resonance imaging (MRI). CAD systems can be used for the detection and diagnosis of breast tumors as a “second opinion” review complementing the radiologist's review. CAD systems have many common parts, such as image preprocessing, tumor feature extraction, and data classification that are mostly based on machine‐learning (ML) techniques. In this review article, we describe applications of ML‐based CAD systems in MRI covering the detection of diagnostically challenging lesions of the breast such as nonmass enhancing (NME) lesions, and furthermore discuss how multiparametric MRI and radiomics can be applied to the study of NME, including prediction of response to neoadjuvant chemotherapy (NAC). Since ML has been widely used in the medical imaging community, we provide an overview about the state‐of‐the‐art and novel techniques applied as classifiers to CAD systems. The differences in the CAD systems in MRI of the breast for several standard and novel applications for NME are explained in detail to provide important examples, illustrating: 1) CAD for detection and diagnosis, 2) CAD in multiparametric imaging, 3) CAD in NAC, and 4) breast cancer radiomics. We aim to provide a comparison between these CAD applications and to illustrate a global view on intelligent CAD systems based on machine and deep learning in MRI of the breast.Level of Evidence2Technical Efficacy Stage2

Development of an intelligent CAD system for mass detection in mammographic images

Mammography is a very useful tool to diagnose breast cancer in early stages when it is easier to treat. There are two types of evidence that radiologists look for in a mammogram, calcifications and the existence of masses. In this study, an intelligent computer-aided diagnosis system is proposed for the detection of masses in mammographic images regardless of their nature. The proposed method uses a combination of extended maxima transformations, having different threshold values, in order to find suitable internal and external markers for a marker-based watershed segmentation. After segmentation, a two-stage classifier is used to distinguish the masses better from the healthy breast tissue. A feature vector based mainly on contrast and texture features is calculated and two alternative approaches, a Bayesian classifier and a support vector machine (SVM) with Gaussian kernel function, are implemented for further reduction of the false positive areas. The system was evaluated using the data from two online databases. Specifically, 73 mammographic images from the new curated breast imaging subset of digital database for screening mammography (CBIS-DDSM) database and all the mammographic images that contain masses from the mini-mammographic image analysis society (MIAS) database were used. The overall sensitivity, in both datasets, was near 80% when the Bayesian classifier was used and above 85% when the SVM was applied.

Improve differentiation of Breast mass using fuzzy segmentation method

Abstract Breast cancer is one of the almost public types of cancer in women. Breast masses generally classified to cystic and solid masses and in both, there are many subtypes. So that understanding exact types of them is useful for better treatment of the patient. There are clinic invasive techniques for that but image processing usually seems helpful to get same or better results. Furthermore, an intelligent computer-aided diagnosis system or proper algorithm can be very helpful for radiologist in detecting and diagnosing abnormal cases earlier and faster than typical screening methods. One of the most important image processing is Edge detection which targets towards image understanding. This study tried to improve the early detection of breast masses (cystic/solid) from 2D ultrasound scanning, by running the resulted images of the abnormal cases in image enhancement, edge detection, furthermore comparing between the resulted 2D images by 3D images, Elastography, and then thermal detection of breast mass images. Four cases of different abnormalities taken from 783 patients had been scanned in Harer hospital by 2D ultrasound B- Mode Linear probe with 7.2 MHz frequency, 47 of them diagnosed as breast mass. The images were enhanced by adaptive histogram equalization (AHE) which usually given proper contrast enhancement. Then appropriate conditions were chosen for Fuzzy set theory to give us proper edge detection. The result was the obtained 2D images are much more clear for diagnosis after running into image processing, the mass easily can be detected and classified into the correct type without using 3D high-cost ultrasound.

NavI-CAD : 知的ナビゲーション診断支援システム

NavI-CAD : 知的ナビゲーション診断支援システム

Computer Aided System for Detection and Classification of Breast Cancer

Breast cancer is one of the most important causes of death among all type of cancers for grown-up and older women, mainly in developed countries, and its rate is rising. Since the cause of this disease is not yet known, early detection is the best way to decrease the breast cancer mortality. At present, early detection of breast cancer is attained by means of mammography. An intelligent computer-aided diagnosis system can be very helpful for radiologist in detecting and diagnosing cancerous cell patterns earlier and faster than typical screening programs. This paper proposes a computer aided system for automatic detection and classification of breast cancer in mammogram images. Intuitionistic Fuzzy C-Means clustering technique has been used to identify the suspicious region or the Region of Interest automatically. Then, the feature data base is designed using histogram features, Gray Level Concurrence wavelet features and wavelet energy features. Finally, the feature database is submitted to self-adaptive resource allocation network classifier for classification of mammogram image as normal, benign or malignant. The proposed system is verified with 322 mammograms from the Mammographic Image Analysis Society Database. The results show that the proposed system produces better results.

SOM Based Visualization Technique For Detection Of Cancerous Masses In Mammogram

Breast cancer is the most common form of cancer in women. An intelligent computer-aided diagnosis system can be very helpful for radiologist in detecting and diagnosing micro calcifications patterns earlier and faster than typical screening programs. In this paper, we present a system based on gabor filter based enhancement technique and feature extraction techniques using texture based segmentation and SOM(Self Organization Map) which is a form of Artificial Neural Network(ANN) used to analyze the texture features extracted. SOM determines which texture feature has the ability to classify benign, malignant and normal cases. Watershed segmentation technique is used to classify cancerous region from the non cancerous region. We have investigated and analyzed a number of feature extraction techniques and found that a combination of ten features, such as Cor-relation, Cluster Prominence, Energy, Entropy, Homogeneity, Difference variance, Difference Entropy, Information Measure, and Normalized are calculated. These features gives the distribution of tonality information and was found to be the best combination to distinguish a benign micro calcification pattern from one that is malignant and normal. The system was developed on a Windows platform. It is an easy to use intelligent system that gives the user options to diagnose, detect, enlarge, zoom, and measure distances of areas in digital mammograms. Further Using Linear Filtering Technique and the Texture Features as Mask are convolved with the segmented image .The tumor is detected using the above method and using watershed segmentation, a fair segmentation is obtained The artificial neural network with unsupervised learning together with texture based approach leads to the accuracy and positive predictive value of each algorithm were used as the evaluation indicators. 121 records acquired from the breast cancer patients at the MIAS database. The results revealed that the accuracies of texture based unsupervised learning has 0.9534 (sensitivity 0.98716 and specificity 0.9582 which was detected thorough the ROC. The results showed that the gabor based unsupervised learning described in the present study was able to produce accurate results in the classification of breast cancer data and the classification rule identified was more acceptable and comprehensible.

English

English

Intelligent computer-aided diagnosis system for chest radiography

We present an intelligent computer-aided diagnosis (ICAD) system for chest radiography to merge radiologic findings or the extracted features into a diagnosis using Multiple Classification Ripple Down Rule (MCRDR) and fuzzy functions. The ICAD system is a semi-automatic system since the abnormalities in chest radiographs could be detected by a combination of the automatic system and radiologists. Based on these abnormalities, the ICAD system suggests the possible disease states.

A computer-aided diagnosis system for digital mammograms based on fuzzy-neural and feature extraction techniques.

An intelligent computer-aided diagnosis system can be very helpful for radiologist in detecting and diagnosing microcalcifications' patterns earlier and faster than typical screening programs. In this paper, we present a system based on fuzzy-neural and feature extraction techniques for detecting and diagnosing microcalcifications' patterns in digital mammograms. We have investigated and analyzed a number of feature extraction techniques and found that a combination of three features, such as entropy, standard deviation, and number of pixels, is the best combination to distinguish a benign microcalcification pattern from one that is malignant. A fuzzy technique in conjunction with three features was used to detect a microcalcification pattern and a neural network to classify it into benign/malignant. The system was developed on a Windows platform. It is an easy to use intelligent system that gives the user options to diagnose, detect, enlarge, zoom, and measure distances of areas in digital mammograms.