Mammogram Classification Using Nonsubsampled Contourlet Transform and Gray-Level Co-Occurrence Matrix

Breast cancer is one of the common malignancies among females in Saudi Arabia and has also been ranked as the one most prevalent and the number two killer disease in the country. However, the clinical diagnosis process of any disease such as breast cancer, coronary artery diseases, diabetes, COVID-19, among others, is often associated with uncertainty due to the complexity and fuzziness of the process. In this work, a fuzzy neural network expert system with an improved gini index random forest-based feature importance measure algorithm for early diagnosis of breast cancer in Saudi Arabia was proposed to address the uncertainty and ambiguity associated with the diagnosis of breast cancer and also the heavier burden on the overlay of the network nodes of the fuzzy neural network system that often happens due to insignificant features that are used to predict or diagnose the disease. An Improved Gini Index Random Forest-Based Feature Importance Measure Algorithm was used to select the five fittest features of the diagnostic wisconsin breast cancer database out of the 32 features of the dataset. The logistic regression, support vector machine, k-nearest neighbor, random forest, and gaussian naïve bayes learning algorithms were used to develop two sets of classification models. Hence, the classification models with full features (32) and models with the 5 fittest features. The two sets of classification models were evaluated, and the results of the evaluation were compared. The result of the comparison shows that the models with the selected fittest features outperformed their counterparts with full features in terms of accuracy, sensitivity, and sensitivity. Therefore, a fuzzy neural network based expert system was developed with the five selected fittest features and the system achieved 99.33% accuracy, 99.41% sensitivity, and 99.24% specificity. Moreover, based on the comparison of the system developed in this work against the previous works that used fuzzy neural network or other applied artificial intelligence techniques on the same dataset for diagnosis of breast cancer using the same dataset, the system stands to be the best in terms of accuracy, sensitivity, and specificity, respectively. The z test was also conducted, and the test result shows that there is significant accuracy achieved by the system for early diagnosis of breast cancer.

Optimization and normalization strategies for long term untargeted HILIC-LC-qTOF-MS based metabolomics analysis: Early diagnosis of breast cancer

To assist specialists in detecting breast cancer on mammograms with better accuracy and less time consuming, this paper proposes an approach based on improved sunflower optimization algorithm (ISFO) and extreme learning machine (ELM). Firstly, features were extracted by using lifting scheme and gray-level co-occurrence matrix (GLCM). Then, the parameters of ELM were optimized by (ISFO) to obtain the final classification results. Finally, in order to avoid overfitting, the proposed model’s performance was evaluated with k-fold random stratified cross validation, and the experiments compared the model with other models on MIAS datasets. The experimental results show that the proposed model has higher classification accuracy, shorter learning time and stronger robustness on mammograms classification task. Thus, this method could be a promising application in bio-medical and provide a basis for the early diagnosis of breast cancer.

Breast cancer is the primary malignant tumor that endangers women's health. The incidence of breast cancer is increasing rapidly in recent years. Accurate disease evaluation before treatment is the key to the selection of treatment options. Biomedical imaging technology plays an irreplaceable role in the diagnosis and staging of tumors. Various imaging methods can provide excellent temporal and spatial resolution from multiple levels and perspectives and have become one of the most commonly used means of breast cancer early detection. With the development of radiomics, it has been found that early imaging diagnosis of breast cancer plays an important guiding role in clinical decision-making. The purpose of this study is to explore the characteristics of various breast cancer imaging technologies, promote the development of individualized accurate diagnosis and treatment of imaging, and improve the clinical application value of radiomics in the early diagnosis of breast cancer.

Purpose: A reliable non-invasive approach for accurate staging fibrosis is still under development. In this study, we utilized a computer aided diagnosis (CAD) system to analyze equilibrium phase magnetic resonance (MR) images in a rat model to determine relative accuracy of staging liver fibrosis with CAD. Methods: Experimental rats were injected with a mixture of CCl4 to generate varying stages of fibrosis and equilibrium phase images of rats were acquired. All rats were grouped based on histological stages (F0, F1, F2, F3 and F4). During the process of CAD, the first step was manual section of region of interest (ROI) with 10*10 pixels guided by histology. Next, Lloyd’s algorithm and linear normalization were both used to compress ROIs to 256 gray level. Then, 80 texture features based on the gray level co-occurrence matrix were extracted. Lastly, the back-propagation (BP), linear, k-nearest neighbor and support vector machine classifiers were individually used to classify F0-F4. Results: With histological staging as a reference, we found that performance of the BP classifier based on Lloyd’s algorithm in staging fibrosis was better than that of linear normalization. Staging accuracy rates of the former were 67.27%, 80.26% and 79.41% for F0 versus F3, F2 versus F4 and F3 versus F4, respectively, secondary to none of the other classifiers. Conclusions: The assessment of equilibrium phase MR images with CAD can effectively stage rat liver fibrosis. This article highlights progress in developing a reliable non-invasive liver fibrosis staging procedure.

Since reporting cases of breast cancer are on the rise all over the world. Especially in regions such as Pakistan, Saudi Arabia, and the United States. Efficient methods for the early detection and diagnosis of breast cancer are needed. The usual diagnosis procedures followed by physicians has been updated with modern diagnostic approaches that include computer-aided support for better accuracy. Machine learning based practices has increased the accuracy and efficiency of medical diagnosis, which has helped save lives of many patients. There is much research in the field of medical imaging diagnostics that can be applied to the variety of data such as magnetic resonance images (MRIs), mammograms, X-rays, ultrasounds, and histopathological images, but magnetic resonance (MR) and mammogram imaging have proved to present the promising results. The proposed paper has presented the results of classification algorithms over Breast Cancer (BC) mammograms from a novel dataset taken from hospitals in the Qassim health cluster of Saudi Arabia. This paper has developed a novel approach called the novel spectral extraction algorithm (NSEA) that uses feature extraction and fusion by using local binary pattern (LBP) and bilateral algorithms, as well as a support vector machine (SVM) as a classifier. The NSEA with the SVM classifier demonstrated a promising accuracy of 94% and an elapsed time of 0.68 milliseconds, which were significantly better results than those of comparative experiments from classifiers named Naïve Bayes, logistic regression, K-Nearest Neighbor (KNN), Gaussian Discriminant Analysis (GDA), AdaBoost and Extreme Learning Machine (ELM). ELM produced the comparative accuracy of 94% however has a lower elapsed time of 1.35 as compared to SVM. Adaboost has produced a fairly well accuracy of 82%, KNN has a low accuracy of 66%. However Logistic Regression, GDA and Naïve Bayes have produced the lowest accuracies of 47%, 43% and 42%.

In the era of modern technology, object detection using the Gray Level Co-occurrence Matrix (GLCM) extraction method plays a crucial role in object recognition processes. It finds applications in real-time scenarios such as security surveillance and autonomous vehicle navigation, among others. Computational efficiency becomes a critical factor in achieving real-time object detection. Hence, there is a need for a detection model with low complexity and satisfactory accuracy. This research aims to enhance computational efficiency by selecting appropriate features within the GLCM framework. Two classification models, namely K-Nearest Neighbours (K-NN) and Support Vector Machine (SVM), were employed, with the results indicating that K-Nearest Neighbours (K-NN) outperforms SVM in terms of computational complexity. Specifically, K-NN, when utilizing a combination of Correlation, Energy, and Homogeneity features, achieves a 100% accuracy rate with low complexity. Moreover, when using a combination of Energy and Homogeneity features, K-NN attains an almost perfect accuracy level of 99.9889%, while maintaining low complexity. On the other hand, despite SVM achieving 100% accuracy in certain feature combinations, its high or very high complexity can pose challenges, particularly in real-time applications. Therefore, based on the trade-off between accuracy and complexity, the K-NN model with a combination of Correlation, Energy, and Homogeneity features emerges as a more suitable choice for real-time applications that demand high accuracy and low complexity. This research provides valuable insights for optimizing object detection in various applications requiring both high accuracy and rapid responsiveness.

A new approach to mass detection in mammography is presented. The main obstacle of building a mass detection system is the similar appearance between masses and density tissues in breast. Hence, the various features of the extracted regions of interest (ROIs) are analyzed by synthesis. Then the support vector machine (SVM), which is designed later to distinguish masses from normal areas, is employed to classify these ROIs exactly. To further improve the performance of SVM, the relevance feedback (RF) is introduced to filter out the false positives. The experimental results illustrate that SVM classifier can effectively detect the mass areas, and the RF-SVM scheme can be efficiently incorporated into this learning framework to further improve detection performance.

Lung cancer is a malignant disease of the bronchial tubes.The progress of the disease is usually rapid in cases of symptomatic small-cell and non-small-cell lung carcinomas.Early detection of the malignant and precancerous lesions is mandatory for a successful treatment.Autofluorescence bronchoscopy (AFB) is used for the detection of degenerated tissue on the inner surfaces of the bronchial tubes [1].In cases of a positive AFB finding, a laboratory analysis of a tissue sample is used to confirm or reject the result.AFB was developed from white-light bronchoscopy (WLB), which was used as a diagnostic tool.In contrast to WLB, AFB utilizes blue light (wavelengths of 380 nm to 460 nm) [2] to [4].Precancerous (early-stage) lesions are differentiated from healthy bronchial tissue based on the differences between the fluorescent light that is reflected.In the subsequent pathophysiological examination, dysplastic lesions, metaplastic lesions, and carcinomas in situ are characterized by more layers of cells, with diameters ranging from micrometers to millimeters.They are differentiated from healthy tissue in a pathological examination by the formation of veins under the basal membrane [1] and different concentrations of fluorophores.AFB utilizes the difference in the fluoroscopic properties of malignant and nonmalignant tissues to detect malignancy, making it a superior diagnostic procedure to WLB.In AFB, a healthy mucus membrane emits more green than red light, and unhealthy tissue emits more red than green.Only a little blue light is reflected, either from the healthy or unhealthy tissue.Different implementations of stimuli and sensors can add their particularities to the general picture of fluorescent bronchoscopy.Many clinical studies have confirmed the utility of AFB in the detection of cellular changes in earlystage lung cancer, with noninvasive neoplastic lesions, small lesions, preneoplastic lesions, and neoplastic changes in tissue detected using state-of-the-art AFB diagnostic systems.Although such detection is possible using WLB, its performance is not as good as that of AFB [2], [5] to [7].In addition to its utilization of changes in fluorescence in the detection of

In this paper, we propose a Computer-Aided Detection (CAD) system based on the novel Multi-Classifier Fusion-based Classification Model (MCFM) of the breast lesions using textural features in Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI). The proposed system consists of four principal phases: Region Of Interest (ROI) segmentation, feature extraction, fusion and selection and finally Classification. We have extracted a complementary set of features from the DCE-MRI database on two statistical descriptors such as the Gray Level Co-occurrence Matrix (GLCM) and Local Binary Pattern (LBP) methods. Then, we have concatenated the extracted features using Serial method to exploit the complementary between these sets. Next, we have applied a features selection phase through Principal Component Analysis (PCA) and Feature Subset Selection (FSS) techniques to remove redundant information and improve the overall performance of breast DCE-MRI in a computer-aided system. For the classification, we have used a set of classifiers (K-Nearest Neighbors (K-NN), Support Vector Machines (SVM) and Multilayer Perceptron-Artificial Neural Network (MLP-ANN)). Then, a fusion Model between them using several metrics is applied. Experimental validation is performed over an MRI dataset, which is comprised of 286 patients with 143 Masses and 143 No Masses (Normal) breast tissues. These experiments report encouraging performances for mass detection, achieved using ten-fold cross-validation model, in terms of the averaged measurements of different metrics such as Sensitivity, Specificity, Area Under Curve (AUC) and Accuracy, which are 0.8972, 0.9572, 0.9630 and 0.9501, respectively. The proposed DCE-MRI analysis model can offer an efficient system for radiologists to identify the breast tissues.

Medical-image segmentation is of primary importance in the development of computer assisted detection (CAD) of mammographic masses. Image segmentation is used for finding regions of interest (ROI) within mammograms that are further processed to estimate their likelihood of being a potential lesion or mass. This task can be stated as follows: €œA gray-scale image is represented as a 1D array X = (x1, x2,...,xN), where xn is an input feature for pixel n and N is the total number of pixels in the image. The input feature vector xn may be a D dimensional vector or simply the gray-scale value of the pixel n. Let the underlying true segmentation of the image be denoted as Y = (y1, y2,..., yN). It is assumed that the number of classes is predetermined as a set of known class labels I‰l, where l Iµ {1,..., L{ and therefore the class label of pixel n is indicated as y n ∈{I‰ l } L l=1 . The task is to predict the A·n estimate of the segmentation for each pixel. In an automated system, such likelihood estimates are generated using a classifier that takes as input some features that describe region properties, e.g., image texture. The segmentation of digital mammograms is a difficult task. A number of different approaches have been adopted in this area with varying success. Broadly speaking, image segmentation can be carried out either as: (1) image preprocessing step with the main objective of identifying regions of interest (spatially coherent collection of pixels within the image) and a subsequent feature extraction and classification step is required to characterize a given region of interest as either €œnormal€ or €œsuspicious€; or (2) part of the overall classification scheme, where the likelihood of each pixel being €œsuspicious is calculated to generate a probability image using pixel-based features as input to a classifier. The probability image is thereafter thresholded to give both the segmentation and classification output. The main difference between (1) and (2) is that in the first case, image segmentation is needed before classification can be performed; whereas in the second case, classification and segmentation are both based on thresholding probability estimates of pixels and are achieved at the same time. The image segmentation approaches, with the aim of finding regions of interest, were first attempted in digital mammography with varying techniques, including global thresholding, region growing, region clustering, template matching, edge operators and filtering, and edge-based hybrid methods. Image segmentation is developed from pixel-based probability estimation is underpinned by modeling features using parametric methods, Gaussian mixture modeling, Markov random fields, and other methods. A detailed discussion on mammogram-based image segmentation with their algorithms is available in Singh and Bovis. Experience shows that it is quite difficult to select an outright winning image-segmentation method, since different approaches have been shown to be suitable for different data sets and show varied performance under different experimental conditions. However, there is a consensus that image segmentation in medical imaging is most successful when explicitly using a priori information about the problem to be studied.

Breast cancer risk assessment and early diagnosis using Principal Component Analysis and support vector machine techniques

BackgroundEarly diagnosis is a key determinant of breast cancer prognosis and survival. More than half of breast cancer cases are diagnosed at an advanced stage in Ethiopia, and the barriers to early diagnosis in this country are not well understood. We aimed to identify the perceived barriers to early diagnosis of breast cancer from the perspective of patients and health care providers in south and southwestern Ethiopia.MethodsA qualitative study was conducted from March to April 2018 using in-depth interviews of breast cancer patients and breast cancer health care providers from six public hospitals located in urban and rural areas of south and southwestern Ethiopia. All participants provided verbal consent before participating. A thematic analysis was performed using Open Code 4.02.ResultsTwelve breast cancer patients and thirteen health care providers were included in the study. Patient and health-system related barriers to early diagnosis of breast cancer were identified. Patient-related barriers were lack of knowledge and awareness of breast cancer, belief in traditional medicine and religious practices for treatment, and lack of social and financial support to seek care at a medical facility. Health-system related barriers were misdiagnosis of breast cancer, long distance to referral facilities, high cost of diagnostic services, long waiting time for diagnostic tests, and lack of screening and diagnostic tests in local facilities.ConclusionsEarly diagnosis of breast cancer is affected by multiple barriers in south and southwestern Ethiopia. Awareness campaigns and education about the disease, prevention, and early detection are needed to increase early diagnosis of breast cancer. Opportunities exist to improve early diagnosis and timely treatment in rural areas.

The second largest disease leading to the death of women is Breast Cancer. To decrease mortality rate in women breast cancer should be diagnosed early. Digital mammography screening plays vital role for early diagnosis of breast cancer. This work presents a method for two types of classification of breast tissues in mammograms. First type of classification uses texture descriptors like Local Binary Pattern (LBP) and its variants such as Local Quinary Pattern (LQP) and Local Ternary Pattern (LTP) to extract the features for the classification of mammograms into normal-abnormal. Second type of classification employs discrete wavelet transform and LBP variants to classify breast tissues into benign-malignant. SVM classifier is used for the validation of our methods. The work was carried out by considering 48 ROIs (Region of Interest) or breast tissues segmented from mammograms. We achieved 100% accuracy in classifying Region of interests into normal and abnormal using LBP and its variants. The highest accuracy of 82.19% is achieved in classifying abnormal Region of Interests into benign and malignant using discrete wavelet transform and LBP variants.

Background: Mammography is commonly utilised for the detection of breast cancer, but it has some drawbacks like inaccurate findings. There is a good possibility of improvements in diagnostic accuracy by ML algorithms. Preventive healthcare requires early cervical cancer screening. Machine learning techniques are used in a variety of ways to analyse data on breast cancer. This study evaluated RF and SVM models for early identification of breast cancer. The results can potentially be improved by the use of comparative analysis. It is necessary to consider the significant contribution of screening programs of cancers. The results obtained in this paper can be utilized as a reference to enhance healthcare policy. Finally, a faster and more accurate detection of cancer can save lives. Method: This research presents computational machine learning (ML) experiments to improve the detection accuracy of breast cancer. It proposes two computational algorithms: RF and SVM to measure the precision of breast cancer prediction. The WDBC dataset is taken as input for analysis. It consists of 569 cases and 30 patient features. Typical data preparation methods include the division of training and testing datasets and normalising the data. The Python language is used to create a Random Forest (RF) classifier and a Support Vector Machine (SVM) algorithm. The model’s hyperparameters are done using grid search and cross-validation methods. The models are evaluated by performance indicators. Results: RF classifier attained an accuracy rate of 96%, which confirms its position as an essential tool for the early identification of diseases. The SVM classifier was able to obtain an overall accuracy of 95%, which also indicates significant potential for early detection techniques. Finally, the findings reveal RF and SVM are equally useful in the early diagnosis of breast cancer. Each approach has specific advantages and disadvantages.