Breast Tumor Detection and Classification in Mammogram Images Using Modified YOLOv5 Network

Breast cancer incidence has been rising steadily during the past few decades. It is the second leading cause of death in women. If it is diagnosed early, there is a good possibility of recovery. Mammography is proven to be an excellent screening technique for breast tumor diagnosis, but its detection and classification in mammograms remain a significant challenge. Previous studies’ major limitation is an increase in false positive ratio (FPR) and false negative ratio (FNR), as well as a drop in Matthews correlation coefficient (MCC) value. A model that can lower FPR and FNR while increasing MCC value is required. To overcome prior research limitations, a modified network of YOLOv5 is used in this study to detect and classify breast tumors. Our research is conducted using publicly available datasets Curated Breast Imaging Subset of DDSM (CBIS-DDSM). The first step is to perform preprocessing, which includes image enhancing techniques and the removal of pectoral muscles and labels. The dataset is then annotated, augmented, and divided into 60% for training, 30% for validation, and 10% for testing. The experiment is then performed using a batch size of 8, a learning rate of 0.01, a momentum of 0.843, and an epoch value of 300. To evaluate the performance of our proposed model, our proposed model is compared with YOLOv3 and faster RCNN. The results show that our proposed model performs better than YOLOv3 and faster RCNN with 96% mAP, 93.50% MCC value, 96.50% accuracy, 0.04 FPR, and 0.03 FNR value. The results show that our suggested model successfully identifies and classifies breast tumors while also overcoming previous research limitations by lowering the FPR and FNR and boosting the MCC value.

Detecting the Speed Change Intention from EEG Signals: From the Offline and Pseudo-Online Analysis to an Online Closed-Loop Validation

Control of assistive devices by voluntary user intention is an underdeveloped topic in the Brain–Machine Interfaces (BMI) literature. In this work, a preliminary real-time BMI for the speed control of an exoskeleton is presented. First, an offline analysis for the selection of the intention patterns based on the optimum features and electrodes is proposed. This is carried out comparing three different classification models: monotonous walk vs. increasing and decreasing change speed intentions, monotonous walk vs. only increasing intention, and monotonous walk vs. only decreasing intention. The results indicate that, among the features tested, the most suitable parameter to represent these models are the Hjorth statistics in alpha and beta frequency bands. The average offline classification accuracy for the offline cross-validation of the three models obtained is 68 ± 11%. This selection is also tested following a pseudo-online analysis, simulating a real-time detection of the subject’s intentions to change speed. The average results indices of the three models during this pseudoanalysis are of a 42% true positive ratio and a false positive rate per minute of 9. Finally, in order to check the viability of the approach with an exoskeleton, a case of study is presented. During the experimental session, the pros and cons of the implementation of a closed-loop control of speed change for the H3 exoskeleton through EEG analysis are commented.

Tracing Hazardous Materials in Registered Records: A Case Study of Demolished and Renovated Buildings in Gothenburg

Hazardous materials encountered during building renovation or demolition processes not only result in uncertainty in cost estimation and the lead time but also hampers material recyclability and reuse. Therefore, the paper discusses the possibility of predicting the extent of the hazardous materials, including asbestos, PCB, mercury, and CFC, through data mining techniques based on registered records. Pre-demolition audits contain observation data that can be used as a sample for statistical prediction through careful processing. By developing an innovative approach of merging data from environmental inventories with building registers, the positive ratio of remaining hazardous materials in the Gothenburg building stock can be estimated. The study highlights the challenges of creating a training dataset by completing information from the existing environmental inventory, providing new insight into digital protocol development for enhancing material circularity.

Essentials of Predicting Epileptic Seizures Based on EEG Using Machine Learning: A Review

Objective: Epilepsy is one of the chronic diseases, which requires exceptional attention. The unpredictability of the seizures makes it worse for a person suffering from epilepsy. Methods: The challenge to predict seizures using modern machine learning algorithms and computing resources would be a boon to a person with epilepsy and its caregivers. Researchers have shown great interest in the task of epileptic seizure prediction for a few decades. However, the results obtained have not clinical applicability because of the high false-positive ratio. The lack of standard practices in the field of epileptic seizure prediction makes it challenging for novice ones to follow the research. The chances of reproducibility of the result are negligible due to the unavailability of implementation environment-related details, use of standard datasets, and evaluation parameters. Results: Work here presents the essential components required for the prediction of epileptic seizures, which includes the basics of epilepsy, its treatment, and the need for seizure prediction algorithms. It also gives a detailed comparative analysis of datasets used by different researchers, tools and technologies used, different machine learning algorithm considerations, and evaluation parameters. Conclusion: The main goal of this paper is to synthesize different methodologies for creating a broad view of the state-of-the-art in the field of seizure prediction.

A characteristic MRI finding to diagnose a partial tear of the medial meniscus posterior root: an ocarina sign

Background Diagnosing partial tears of the medial meniscus (MM) posterior root is difficult. The aim of this study was to evaluate diagnostic values involved in conventional magnetic resonance imaging (MRI) features of MM posterior root tears (MMPRTs) and find other MRI-based findings in patients with partial MMPRTs. Methods Eighteen patients who had arthroscopically confirmed partial MMPRTs were included. As a control, 18 patients who underwent partial meniscectomy for other types of MM tears were evaluated. Isolated partial MMPRTs were classified into the following three types: type A, accurate partial stable tear (cleavage < 1/2 of root width); type B, bridged unstable root tear (cleavage ≥ 1/2 of root width); type C, complex horn tear expanded to the root. Conventional MRI-based findings of MMPRTs were evaluated between two groups (n = 23). Posterior root irregularity, bone marrow spot, and ocarina-like appearance showing several condensed circles in triangular meniscal horn (ocarina sign) were also evaluated. Results Posterior root irregularity and bone marrow spot were frequently observed in the partial MMPRTs (47.8%), compared with the other MM tears (P = 0.007 and 0.023, respectively). The ocarina sign was detected in 69.6% of patients with partial MMPRTs. A significant difference between two groups was observed in a positive ratio of ocarina sign (P < 0.001). Types A, B, and C of the partial tear/damage were observed in three, eight, and seven patients, respectively. The ocarina sign was the most common MRI finding in each type of partial MMPRT. Conclusions This study demonstrated that a characteristic MRI finding, “ocarina sign,” was frequently observed in patients with partial tear/damage of the MM posterior root. The ocarina sign was the most common MRI finding in several types of partial MMPRTs. Our results suggest that the ocarina sign may be useful to diagnose unnoticed partial MMPRTs. Level of evidence: IV, retrospective comparative study.

Primary Epithelioid Rhabdomyosarcoma of the Liver: Case Report of an Unusual Histological Variant

Rhabdomyosarcoma (RMS) is a soft tissue tumor with striated muscle cell differentiation. It mostly occurs in children. While it can affect any part of the body, it commonly involves the urogenital organs, head and neck including the parameninges and orbit, and limbs. We describe an adult case of primary epithelioid RMS of the liver. A 71-year-old man presented with a 5.6 cm liver mass. Tumor histology revealed diffuse proliferation of small epithelioid cells and focal spindle cells. The tumor cells were immunohistochemically positive for myogenin (positive ratio 30%), desmin, Myo D1, and CD56. The tumor weakly expressed MDM2 and did not express CDK4. This suggested that dedifferentiated liposarcoma with a rhabdomyosarcomatous component was unlikely. There was no fusion gene of PAX3-FKHR or PAX7- FKHR to indicate alveolar RMS by RT-PCR. Subsequently, RNA Pan-Cancer Targeted sequencing was performed for 1385 genes revealed a single base substitution (c.742C>T) in TP53 that changes an amino acid (p.Arg248Trp). No fusion gene was found. No other primary RMS lesions were detected aside from the liver lesion. The tumor was diagnosed as a primary epithelioid RMS of the liver. His RMS already metastasized to the lymph nodes of the entire body. The patient declined further therapy and died one year later. This was the first case report of a primary epithelioid RMS of the liver.

Surveillance and Reduction Control of Escherichia coli and Diarrheagenic E. coli During the Pig Slaughtering Process in China

The microbial contamination of pork during the slaughter process, especially that of the hygiene indicator bacteria, Escherichia coli, is closely related to the safety and quality of the meat. Some diarrheagenic E. coli can cause serious foodborne diseases, and pose a significant threat to human life and health. In order to ascertain the current status of E. coli and diarrheagenic E. coli contamination during the pig slaughter process in China, we conducted thorough monitoring of large-sized slaughterhouses, as well as small- or medium-sized slaughterhouses, in different provinces of China from 2019 to 2020. The overall positive rate of E. coli on the pork surface after slaughter was very high (97.07%). Both the amount of E. coli contamination and the positive ratio of diarrheagenic E. coli in large-sized slaughterhouses (7.50–13.33 CFU/cm2, 3.44%) were lower than those in small- or medium-sized slaughterhouses (74.99–133.35 CFU/cm2, 5.71%). Combined with the current status of sanitary control in slaughterhouses, we determined that pre-cooling treatment significantly reduced E. coli and diarrheagenic E. coli in pork after slaughter, while microbiological testing reduced E. coli. Based on our monitoring data, China urgently needs to establish relevant standards to better control microbial contamination during pig slaughtering progress. This study provided a theoretical basis for the hygiene quality management of the pig slaughter industry in China.

Computer Vision-Based Microcalcification Detection in Digital Mammograms Using Fully Connected Depthwise Separable Convolutional Neural Network

Microcalcification clusters in mammograms are one of the major signs of breast cancer. However, the detection of microcalcifications from mammograms is a challenging task for radiologists due to their tiny size and scattered location inside a denser breast composition. Automatic CAD systems need to predict breast cancer at the early stages to support clinical work. The intercluster gap, noise between individual MCs, and individual object’s location can affect the classification performance, which may reduce the true-positive rate. In this study, we propose a computer-vision-based FC-DSCNN CAD system for the detection of microcalcification clusters from mammograms and classification into malignant and benign classes. The computer vision method automatically controls the noise and background color contrast and directly detects the MC object from mammograms, which increases the classification performance of the neural network. The breast cancer classification framework has four steps: image preprocessing and augmentation, RGB to grayscale channel transformation, microcalcification region segmentation, and MC ROI classification using FC-DSCNN to predict malignant and benign cases. The proposed method was evaluated on 3568 DDSM and 2885 PINUM mammogram images with automatic feature extraction, obtaining a score of 0.97 with a 2.35 and 0.99 true-positive ratio with 2.45 false positives per image, respectively. Experimental results demonstrated that the performance of the proposed method remains higher than the traditional and previous approaches.

Epidemiological Characteristics of Pandemic Coronavirus Disease (COVID-19) in Oman

Objectives: The aim of the current study was to describe COVID-19’s epidemiological characteristics in Oman during the initial stages of the outbreak and compare findings with other countries’ reports. Methods: Data were drawn from a descriptive, records-based review of reported cases of COVID-19 collected through the national COVID-19 Surveillance System from February to April 2020. Results: A total of 2,443 confirmed cases were reported during the study period. The overall first-time testing rate for this period was 851.7 per 100,000, the positivity rate was 53.1 (confidence intervals [CI]: 51.0–55.2) and the death rate was 0.32 (CI: 0.20–0.54) per 100,000 population, respectively. The overall national positive ratio was 5.7% and ranged from 2.2–7.1% across various governorates. Muscat Governorate had the highest positive ratio (12.5%). People in the 51–60 year old age group (RR = 1.97), males (RR = 1.24), non-Omanis (RR = 2.33) and those living in Muscat (RR = 2.14) emerged as categories with significant demographic risk for COVID-19 cases when compared to the national average. The mean age was 35.6 ± 13.4. Asymptomatic cases accounted for nearly 16%. Conclusion: The overall rate of COVID-19 cases and deaths were low in Oman compared to the rest of the world during the study period. Keywords: Coronavirus; COVID-19; SARS-CoV2; Epidemiology; Pandemic; Oman.

CLDN15 is a novel diagnostic marker for malignant pleural mesothelioma

Malignant mesothelioma is a cancer with a poor survival rate. It is difficult to diagnose mesotheliomas because they show a variety of histological patterns similar to those of various other cancers. However, since currently used positive markers for mesotheliomas may show false positives or false negatives, a novel mesothelial positive marker is required. In the present study, we screened 25 claudins and found that claudin-15 is expressed in the mesothelial cells. We made new rat anti-human claudin-15 (CLDN15) monoclonal antibodies that selectively recognize CLDN15, and investigated whether CLDN15 is a good positive marker for malignant pleural mesotheliomas (MPMs) using MPM tissue samples by immunohistochemistry and semi-quantification of the expression level using an immunoreactive score (IRS) method. Of 42 MPM samples, 83% were positive for CLDN15. The positive ratio was equal to or greater than other positive markers for MPMs including calretinin (81%), WT-1 (50%), and D2-40 (81%). In 50 lung adenocarcinoma sections, four cases were positive for CLDN15 and the specificity (92%) was comparable with other markers (90–100%). Notably, CLDN15 was rarely detected in 24 non-mesothelial tumors in the tissue microarray (12/327 cases). In conclusion, CLDN15 can be used in the clinical setting as a positive marker for MPM diagnosis.