Early-Stage Detection of Ovarian Cancer Based on Clinical Data Using Machine Learning Approaches.

One of the common types of cancer for women is ovarian cancer. Still, at present, there are no drug therapies that can properly cure this deadly disease. However, early-stage detection could boost the life expectancy of the patients. The main aim of this work is to apply machine learning models along with statistical methods to the clinical data obtained from 349 patient individuals to conduct predictive analytics for early diagnosis. In statistical analysis, Student's t-test as well as log fold changes of two groups are used to find the significant blood biomarkers. Furthermore, a set of machine learning models including Random Forest (RF), Support Vector Machine (SVM), Decision Tree (DT), Extreme Gradient Boosting Machine (XGBoost), Logistic Regression (LR), Gradient Boosting Machine (GBM) and Light Gradient Boosting Machine (LGBM) are used to build classification models to stratify benign-vs.-malignant ovarian cancer patients. Both of the analysis techniques recognized that the serumsamples carbohydrate antigen 125, carbohydrate antigen 19-9, carcinoembryonic antigen and human epididymis protein 4 are the top-most significant biomarkers as well as neutrophil ratio, thrombocytocrit, hematocrit blood samples, alanine aminotransferase, calcium, indirect bilirubin, uric acid, natriumas as general chemistry tests. Moreover, the results from predictive analysis suggest that the machine learning models can classify malignant patients from benign patients with accuracy as good as 91%. Since generally, early-stage detection is not available, machine learning detection could play a significant role in cancer diagnosis

Particle Swarm Optimized Fuzzy CNN With Quantitative Feature Fusion for Ultrasound Image Quality Identification.

Inherently ultrasound images are susceptible to noise which leads to several image quality issues. Hence, rating of an image's quality is crucial since diagnosing diseases requires accurate and high-quality ultrasound images. This research presents an intelligent architecture to rate the quality of ultrasound images. The formulated image quality recognition approach fuses feature from a Fuzzy convolutional neural network (fuzzy CNN) and a handcrafted feature extraction method. We implement the fuzzy layer in between the last max pooling and the fully connected layer of the multiple state-of-the-art CNN models to handle the uncertainty of information. Moreover, the fuzzy CNN uses Particle swarm optimization (PSO) as an optimizer. In addition, a novel Quantitative feature extraction machine (QFEM) extracts hand-crafted features from ultrasound images. Next, the proposed method uses different classifiers to predict the image quality. The classifiers categories ultrasound images into four types (normal, noisy, blurry, and distorted) instead of binary classification into good or poor-quality images. The results of the proposed method exhibit a significant performance in accuracy (99.62%), precision (99.62%), recall (99.61%), and f1-score (99.61%). This method will assist a physician in automatically rating informative ultrasound images with steadfast operation in real-time medical diagnosis

Evaluating the Use of Breast Self-Examination (BSE) for Recognizing Breast Cancer Awareness Among Jordanian Students and Workers in Medical Fields

Ammar A Oglat,1 Tala AbuKhalil,1 Hanan Hasan,2 Israa H Isawi,3 Ahmad A Oqlat,4 Hamad Yahia Abu Mhanna,5 Hanan Fawaz Akhdar6 1Department of Medical Imaging, Faculty of Applied Medical Sciences, The Hashemite University, Zarqa, Jordan; 2Department of Pathology, Microbiology and Forensic Medicine, School of Medicine, The University of Jordan, Amman, Jordan; 3Department of Medicinal Chemistry and Pharmacognosy, Faculty of Pharmacy, Jordan University of Science and Technology, Irbid, Jordan; 4Department of Accident and Emergency Medicine, Faculty of Medicine, Jordan University of Science and Technology, Irbid, Jordan; 5School of Physics, Universiti Sains Malaysia, USM, George Town, Penang, Malaysia; 6Physics Department, College of Science, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi ArabiaCorrespondence: Ammar A Oglat, Email [email protected]: Globally, breast cancer (BC) is the most commonly detected neoplasm in women. Breast self-examination (BSE) is an effective screening technique that enables women to learn about the composition of their breasts and assist in the early identification of any potential breast abnormalities.Objective: This study aimed to assess the degree of BSE knowledge and attention among Jordanian females who are students or professionals in medical disciplines.Methods: Participants’ knowledge about BSE and related issues was assessed using a self-administered questionnaire. The study invites participation from all females aged 18 and above, through both an online and in-person survey. The study extended invitations to female university students in Jordan across academic levels I, II, III, IV, V, and VI. A scoring system was employed, and the statistical analyses were performed using IBM SPSS Statistics (Version 20.0).Results: The study had 946 female participants, with 98.41% of them being single. Low BSE practice was reported among 90.49% of the participants (n = 856) and this demonstrated a weak understanding of BC disease, including its possible risks, methods of detection, diagnosis, treatment, signs and symptoms, as well as knowledge about mammography and other related information. Only 27.27% (n = 258) of participants practice BSE once a month and on a regular basis.Conclusion: BC is considered the most prevalent malignant condition and the second largest cause of cancer-related deaths for women in Jordan. Screening strategies are essential for promptly identifying breast cancer and reducing the associated illness and death rates. It is recommended that women commence performing BSE starting at the age of 18. Furthermore, it is essential to incorporate a learning outcome in the cancer chapters that are directly relevant to the subject of BC and emphasize the significance of BSE for students pursuing a career in the medical area. Keywords: breast cancer knowledge, self-examination, screening, Jorda