Computer aided diagnosis system for breast cancer using deep learning.

The recent rise of big data technology surrounding the electronic systems and developed toolkits gave birth to new promises for Artificial Intelligence (AI). With the continuous use of data-centric systems and machines in our lives, such as social media, surveys, emails, reports, etc., there is no doubt that data has gained the center of attention by scientists and motivated them to provide more decision-making and operational support systems across multiple domains. With the recent breakthroughs in artificial intelligence, the use of machine learning and deep learning models have achieved remarkable advances in computer vision, ecommerce, cybersecurity, and healthcare. Particularly, numerous applications provided efficient solutions to assist radiologists and doctors for medical imaging analysis, which has remained the essence of the visual representation that is used to construct the final observation and diagnosis. Medical research in cancerology and oncology has been recently blended with the knowledge gained from computer engineering and data science experts. In this context, an automatic assistance or commonly known as Computer-aided Diagnosis (CAD) system has become a popular area of research and development in the last decades. As a result, the CAD systems have been developed using multidisciplinary knowledge and expertise and they have been used to analyze the patient information to assist clinicians and practitioners in their decision-making process. Treating and preventing cancer remains a crucial task that radiologists and oncologists face every day to detect and investigate abnormal tumors. Therefore, a CAD system could be developed to provide decision support for many applications in the cancer patient care processes, such as lesion detection, characterization, cancer staging, tumors assessment, recurrence, and prognosis prediction. Breast cancer has been considered one of the common types of cancers in females across the world. It was also considered the leading cause of mortality among women, and it has been increased drastically every year. Early detection and diagnosis of abnormalities in screened breasts has been acknowledged as the optimal solution to examine the risk of developing breast cancer and thus reduce the increasing mortality rate. Accordingly, this dissertation proposes a new state-of-the-art CAD system for breast cancer diagnosis that is based on deep learning technology and cutting-edge computer vision techniques. Mammography screening has been recognized as the most effective tool to early detect breast lesions for reducing the mortality rate. It helps reveal abnormalities in the breast such as Mass lesion, Architectural Distortion, Microcalcification. With the number of daily patients that were screened is continuously increasing, having a second reading tool or assistance system could leverage the process of breast cancer diagnosis. Mammograms could be obtained using different modalities such as X-ray scanner and Full-Field Digital mammography (FFDM) system. The quality of the mammograms, the characteristics of the breast (i.e., density, size) or/and the tumors (i.e., location, size, shape) could affect the final diagnosis. Therefore, radiologists could miss the lesions and consequently they could generate false detection and diagnosis. Therefore, this work was motivated to improve the reading of mammograms in order to increase the accuracy of the challenging tasks. The efforts presented in this work consists of new design and implementation of neural network models for a fully integrated CAD system dedicated to breast cancer diagnosis. The approach is designed to automatically detect and identify breast lesions from the entire mammograms at a first step using fusion models’ methodology. Then, the second step only focuses on the Mass lesions and thus the proposed system should segment the detected bounding boxes of the Mass lesions to mask their background. A new neural network architecture for mass segmentation was suggested that was integrated with a new data enhancement and augmentation technique. Finally, a third stage was conducted using a stacked ensemble of neural networks for classifying and diagnosing the pathology (i.e., malignant, or benign), the Breast Imaging Reporting and Data System (BI-RADS) assessment score (i.e., from 2 to 6), or/and the shape (i.e., round, oval, lobulated, irregular) of the segmented breast lesions. Another contribution was achieved by applying the first stage of the CAD system for a retrospective analysis and comparison of the model on Prior mammograms of a private dataset. The work was conducted by joining the learning of the detection and classification model with the image-to-image mapping between Prior and Current screening views. Each step presented in the CAD system was evaluated and tested on public and private datasets and consequently the results have been fairly compared with benchmark mammography datasets. The integrated framework for the CAD system was also tested for deployment and showcase. The performance of the CAD system for the detection and identification of breast masses reached an overall accuracy of 97%. The segmentation of breast masses was evaluated together with the previous stage and the approach achieved an overall performance of 92%. Finally, the classification and diagnosis step that defines the outcome of the CAD system reached an overall pathology classification accuracy of 96%, a BIRADS categorization accuracy of 93%, and a shape classification accuracy of 90%. Results given in this dissertation indicate that our suggested integrated framework might surpass the current deep learning approaches by using all the proposed automated steps. Limitations of the proposed work could occur on the long training time of the different methods which is due to the high computation of the developed neural networks that have a huge number of the trainable parameters. Future works can include new orientations of the methodologies by combining different mammography datasets and improving the long training of deep learning models. Moreover, motivations could upgrade the CAD system by using annotated datasets to integrate more breast cancer lesions such as Calcification and Architectural distortion. The proposed framework was first developed to help detect and identify suspicious breast lesions in X-ray mammograms. Next, the work focused only on Mass lesions and segment the detected ROIs to remove the tumor’s background and highlight the contours, the texture, and the shape of the lesions. Finally, the diagnostic decision was predicted to classify the pathology of the lesions and investigate other characteristics such as the tumors’ grading assessment and type of the shape. The dissertation presented a CAD system to assist doctors and experts to identify the risk of breast cancer presence. Overall, the proposed CAD method incorporates the advances of image processing, deep learning, and image-to-image translation for a biomedical application

An integrated framework for breast mass classification and diagnosis using stacked ensemble of residual neural networks

Abstract A computer-aided diagnosis (CAD) system requires automated stages of tumor detection, segmentation, and classification that are integrated sequentially into one framework to assist the radiologists with a final diagnosis decision. In this paper, we introduce the final step of breast mass classification and diagnosis using a stacked ensemble of residual neural network (ResNet) models (i.e. ResNet50V2, ResNet101V2, and ResNet152V2). The work presents the task of classifying the detected and segmented breast masses into malignant or benign, and diagnosing the Breast Imaging Reporting and Data System (BI-RADS) assessment category with a score from 2 to 6 and the shape as oval, round, lobulated, or irregular. The proposed methodology was evaluated on two publicly available datasets, the Curated Breast Imaging Subset of Digital Database for Screening Mammography (CBIS-DDSM) and INbreast, and additionally on a private dataset. Comparative experiments were conducted on the individual models and an average ensemble of models with an XGBoost classifier. Qualitative and quantitative results show that the proposed model achieved better performance for (1) Pathology classification with an accuracy of 95.13%, 99.20%, and 95.88%; (2) BI-RADS category classification with an accuracy of 85.38%, 99%, and 96.08% respectively on CBIS-DDSM, INbreast, and the private dataset; and (3) shape classification with 90.02% on the CBIS-DDSM dataset. Our results demonstrate that our proposed integrated framework could benefit from all automated stages to outperform the latest deep learning methodologies

Ensemble Deep Learning Models for Heart Disease Classification: A Case Study from Mexico

Heart diseases are highly ranked among the leading causes of mortality in the world. They have various types including vascular, ischemic, and hypertensive heart disease. A large number of medical features are reported for patients in the Electronic Health Records (EHR) that allow physicians to diagnose and monitor heart disease. We collected a dataset from Medica Norte Hospital in Mexico that includes 800 records and 141 indicators such as age, weight, glucose, blood pressure rate, and clinical symptoms. Distribution of the collected records is very unbalanced on the different types of heart disease, where 17% of records have hypertensive heart disease, 16% of records have ischemic heart disease, 7% of records have mixed heart disease, and 8% of records have valvular heart disease. Herein, we propose an ensemble-learning framework of different neural network models, and a method of aggregating random under-sampling. To improve the performance of the classification algorithms, we implement a data preprocessing step with features selection. Experiments were conducted with unidirectional and bidirectional neural network models and results showed that an ensemble classifier with a BiLSTM or BiGRU model with a CNN model had the best classification performance with accuracy and F1-score between 91% and 96% for the different types of heart disease. These results are competitive and promising for heart disease dataset. We showed that ensemble-learning framework based on deep models could overcome the problem of classifying an unbalanced heart disease dataset. Our proposed framework can lead to highly accurate models that are adapted for clinical real data and diagnosis use

In vitro evaluation of tropical forages from the Sahel for their methanogenic potential

International audienc

Methanogenic potential of tropical forages: in vitro evaluation and prediction from chemical composition

International audienc

A disconcerting false gastric diverticulum mimicking malignancy

Gastric diverticula are the most uncommon form of gastrointestinal diverticula. They can either be of true or false type with different pathogenesis. They may be very challenging to diagnose as symptoms are nonspecific and imaging can simulate a malignant lesion. We report an unusual case of pre-pyloric diverticulum in a 69-year-old man, leading to severe gastric obstruction with a poor general condition. As subsequent endoscopy and imaging were alarming and couldn't exclude malignancy, the patient underwent an antrectomy. The final diagnosis was made on pathological examination. We discuss, through this case, the clinical and pathological features of gastric diverticula with an emphasis on the pathogenesis of this rare entity and the risk of a malignant transformation

The relationship between coagulation disorders and the risk of bleeding in cirrhotic patients

Introduction and Objectives: For long, bleeding in cirrhotic patients has been associated with acquired coagulation disorders. The aim of our study was to investigate the impact of acquired coagulation disorders on bleeding risk in cirrhotic patients. Materials and methods: Blood samples were collected from 51 cirrhotic patients with (H+) or without (H−) bleeding events and 50 controls matched by age and sex. Thrombin generation was assessed as endogenous thrombin potential (ETP). Hemostatic balance was assessed by means of ratios of pro- to anticoagulant factors and by ETP ratio with/without protein C (PC) activator (ETP ratio). Results: Bleeding events occurred in 9 patients (17.6%). Compared with controls, VIII/anticoagulant factors, VII/PC and XII/PC were significantly higher in (H+) patients. No significant difference as regards all ratios across patient groups was detected. ETP ratio was significantly higher in (H+) patients than in controls (p = 0.017). However, there was no significant difference between patient groups as regards ETP ratio. Conclusion: Hemostatic balance is shifted toward a hypercoagulability state even in cirrhotic patients who experienced bleeding. These findings provide evidence against traditional concept of hemostasis-related bleeding tendency in cirrhotic patients

[77] Adult urinary lithiasis and chronic renal insufficiency in 32 cases

Objective: To specify the clinical, metabolic and aetiological characteristics of stone diseases complicated by chronic renal failure. Renal lithiasis is a common, highly recurrent disease that can be complicated by chronic renal failure, which is usually prevented by early diagnosis and adequate medical and surgical management. Methods: Over a 10-year period from 2008 to 2018, we collected 173 patients with a confirmed urolithiasis aetiology, amongst which 32 had chronic renal insufficiency with a creatinine clearance of <60 mL/min at the time of the diagnosis. Results: There were 19 men and 13 women (sex ratio 1.58) with a mean (range) age of 51.59 (32–72) years. The prevalence of renal failure was 18.47%. Two patients had end-stage renal disease. Lithiasis was bilateral in 24 cases and unilateral in eight. In all, 21 patients underwent surgery with nephrectomy in 10, 17 patients had extracorporeal lithotripsy, and four patients had a percutaneous nephrolithotomy. The average time between the onset of lithiasis disease and the aetiological diagnosis was 12 years. In regards to aetiologies we noted: hyperoxaluria in eight cases (primary: five cases, food: three cases), hyperparathyroidism in five cases, a metabolic syndrome in five cases, hyperuricuria in five cases, a lithiasis of infection in six cases (isolated: two cases, associated with a metabolic cause: four cases), cystinuria in two cases, and distal tubular acidosis in one case. Conclusion: The high percentage of chronic renal failure in our patients was the result of late aetiological diagnosis and management. The aggravating factors were infections and nephrectomies