COVID-CLNet; COVID-19 Detection with Compressive Deep Learning Approaches

One of the most serious global health threats is COVID-19 pandemic. The emphasis on increasing the diagnostic capability helps stopping its spread significantly. Therefore, to assist the radiologist or other medical professional to detect and identify the COVID-19 cases in the shortest possible time, we propose a computer-aided detection (CADe) system that uses the computed tomography (CT) scan images. This proposed boosted deep learning network (CLNet) is based on the implementation of Deep Learning (DL) networks as a complementary to the Compressive Learning (CL). We utilize our inception feature extraction technique in the measurement domain using CL to represent the data features into a new space with less dimensionality before accessing the Convolutional Neural Network. All original features have been contributed equally to the new space using a sensing matrix. Experiments performed on different compressed methods show promising results for COVID-19 detection.

A YOLO and convolutional neural network for the detection and classification of leukocytes in leukemia

The developing of deep learning systems that used for chronic diseases diagnosing is challenge. Furthermore, the localization and identification of objects like white blood cells (WBCs) in leukemia without preprocessing or traditional hand segmentation of cells is a challenging matter due to irregular and distorted of nucleus. This paper proposed a system for computer-aided detection depend completely on deep learning with three models computer-aided detection (CAD3) to detect and classify three types of WBC which is fundamentals of leukemia diagnosing. The system used modified you only look once (YOLO v2) algorithm and convolutional neural network (CNN). The proposed system trained and evaluated on dataset created and prepared specially for the addressed problem without any traditional segmentation or preprocessing on microscopic images. The study proved that dividing of addressed problem into sub-problems will achieve better performance and accuracy. Furthermore, the results show that the CAD3 achieved an average precision (AP) up to 96% in the detection of leukocytes and accuracy 94.3% in leukocytes classification. Moreover, the CAD3 gives report contain a complete information of WBC. Finally, the CAD3 proved its efficiency on the other dataset such as acute lymphoblastic leukemia image database (ALL-IBD1) and blood cell count dataset (BCCD).

The value of computer aided detection system FOR mammography in the breast cancer screening: single-center, prospective, randomized clinical trial

Purpose. To assess the influence of mammography mapping with the help of computer-aided detection system (CAD) MammCheck II of our own design on the relapse-free survival (RFS) in breast cancer (BC) patients detected during the combined (mammographic and ultrasound [US]) screening. Materials and methods. 10732 women aged 40-87 years old (mean age: 52.20±8.63) who performed mammography were randomized to the standard screening group (mammography → US of the dense breasts) or to the group of CAD-assisted screening (mammography → CAD → targeted US of suspicious CAD markings, as well as the standard US of the dense breasts; CAD group). The primary endpoint was the 3-years RFS. Results. Totally, in the standard screening group we identified 230 BCs (4.29%), in the CAD group — 248 BCs (4.62%; p>0.05), including 49 (21.20%) и 88 (35.48%) 0-I stage BCs, respectively (p<0.05). Median of the primary tumor size was significantly lower in the CAD group (18 mm) compared to the standard screening group (25 mm; р<0.05). 3-years RFS was significantly higher (87.90%) in the CAD group compared to the standard screening group (81.20%; р<0.05). Conclusion. Breast US after the previous mammography CAD mapping significantly increases the 3-years RFS of women with combined screening-detected BC.

Early TB case detection by community-based mobile X-ray screening and Xpert testing in Balochistan

SETTING: This survey was conducted at 35 sites of 20 cities in 15 districts with low programmatic TB case notifications in the past years in Balochistan.OBJECTIVE: To assess the effectiveness of the systemic community-based screening and diagnosis for early detection of TB; and 2) to describe the characteristics and understand the strengths and weaknesses of the intervention in Balochistan, and sociodemographic factors associated with it.DESIGN: This cross-sectional descriptive study was conducted using a mobile van equipped with a digital X-ray machine with computer-aided detection for TB (CAD4TB) software for screening, followed by confirmatory high sensitivity Xpert® MTB/RIF assay testing.RESULTS: A total of 236 (3.4%) TB cases was detected out of 6,899 screened. About 1,168 (17%) presumptive TB cases were identified and 1,065 (91%) sputum samples were tested on Xpert. Among those diagnosed, 166 (70%) were Mycobacterium tuberculosis-positive and 70 (30%) were with clinical suspicion. Of the sputum samples tested, 87% (923/1065) had a probability score of >70 on CAD4TB.CONCLUSION: Community-based screening with innovative activities, comprising sensitive screening and diagnostic tools, effectively improves TB case detection, which might suffice to reduce the prevalence of TB and break the chain of infection transmission in the at-risk population.

The value of computer aided detection system for mammography in the fatty breast: the results of the single-centered, prospective, randomized clinical trial

Aim. To assess the reasonability to use CAD added to mammography with subsequent targeted ultrasound (US) of CAD markings in patients with low-density (ACR A-В) breasts.Materials and methods. In the prospective study we included 2326 women with low breast density. They were randomized for CAD (MammCheck II of our own design) checking with subsequent targeted US (MMG + CAD group) or without CAD (MMG only group). After the initial screening we performed the 3-year follow-up phase.Results. Totally, during the primary screening in the MMG only group we found 77 breast cancers (BCs) (28,57% of them sized less than 1 cm), in the MMG + CAD group – 69 BCs (36,23% of them sized less than 1 cm), р > 0.05. The suspicious lesion was identified only during the targeted US of the CAD marking in 4 of 25 women in the MMG + CAD group, and all these BCs were below 1 cm in size. During the subsequent follow-up in the MMG only group we found 5 additional BCs, with no such cases in the MMG + CAD group (p < 0.05). Three of these five BCs were retrospectively marked by CAD. The only visible BC that was not marked by CAD was 3 mm in size.Discussion. The overall false positive marking rate was 0.31 и 0.28 per film-screen and digital image, respectively (р > 0.05).Conclusion. The CAD usage added to mammography with subsequent targeted US of markings in patients with low-density (ACR A-В) breast is reasonable due to the significant decrease of the BC rate diagnosed during the 3-year follow-up. This combination detected 77 of the 77 (100.00%) BCs compared to 69 of 74 (93.24%) BCs when only mammography used.