CKD as well as normal control participants. Digital mammograms with appropriate image enhancement techniques will improve breast cancer detection, and thus increase the survival rates. The objectives of this study were to systematically review and compare various image enhancement techniques in digital mammograms for breast cancer detection. A literature search was conducted with the use of three online databases namely, Web of Science, Scopus, and ScienceDirect. Developed keywords strategy was used to include only the relevant articles. A Population Intervention Comparison Outcomes (PICO) strategy was used to develop the inclusion and exclusion criteria. Image quality was analyzed quantitatively based on peak signal-noise-ratio (PSNR), Mean Squared Error (MSE), Absolute Mean Brightness Error (AMBE), Entropy, and Contrast Improvement Index (CII) values. Nine studies with four types of image enhancement techniques were included in this study. Two studies used histogram-based, three studies used frequency-based, one study used fuzzy-based and three studies used filter-based. All studies reported PSNR values whilst only four studies reported MSE, AMBE, Entropy and CII values. Filter-based was the highest PSNR values of 78.93, among other types. For MSE, AMBE, Entropy, and CII values, the highest were frequency-based (7.79), fuzzy-based (93.76), filter-based (7.92), and frequency-based (6.54) respectively. In summary, image quality for each image enhancement technique is varied, especially for breast cancer detection. In this study, the frequency-based of Fast Discrete Curvelet Transform (FDCT) via the UnequiSpaced Fast Fourier Transform (USFFT) shows the most superior among other image enhancement techniques.In summary, image quality for each image enhancement technique is varied, especially for breast cancer detection. In this study, the frequency-based of Fast Discrete Curvelet Transform (FDCT) via the UnequiSpaced Fast Fourier Transform (USFFT) shows the most superior among other image enhancement techniques.Embryologic developmental variants of the thyroid and parathyroid glands may cause cervical anomalies that are detectable in ultrasound examinations of the neck. For some of these developmental variants, molecular genetic factors have been identified. Ultrasound, as the first-line imaging procedure, has proven useful in detecting clinically relevant anatomic variants. The aim of this article was to systematically summarize the ultrasound characteristics of developmental variants of the thyroid and parathyroid glands as well as ectopic thymus and neck cysts. Quantitative measures were developed based on our own findings and the respective literature. Developmental anomalies frequently manifest as cysts that can be detected by cervical ultrasound examinations. Median neck cysts are the most common congenital cervical cystic lesions, with a reported prevalence of 7% in the general population. Besides cystic malformations, developmental anomalies may appear as ectopic or dystopic tissue. Ectopic thyroid tissue is observed in the midline of the neck in most patients and has a prevalence of 1/100,000 to 1/300,000. Lingual thyroid accounts for 90% of cases of ectopic thyroid tissue. Zuckerkandl tubercles (ZTs) have been detected in 55% of all thyroid lobes. Prominent ZTs are frequently observed in thyroid lobes affected by autoimmune thyroiditis compared with normal lobes or nodular lobes (P = 0.006). The correct interpretation of the ultrasound characteristics of these variants is essential to establish the clinical diagnosis. In the preoperative assessment, the identification of these cervical anomalies via ultrasound examination is indispensable. To prevent Alzheimer's disease (AD) from progression to dementia, early prediction and classification of AD plays a crucial role in medical image analysis. In this study, we employed transfer learning technique to classify Magnetic Resonance (MR) images using a pre-trained convolutional neural network (CNN). To address the early diagnosis of AD, we employed computer-assisted technique specifically deep learning (DL) model to detect AD. In particular, we classified Alzheimer's disease (AD), mild cognitive impairment (MCI) and normal control (NC) subjects using whole slide two-dimensional (2D) images. To illustrate this approach, we made use of state-of-the-art CNN base models, i.e., the residual networks ResNet-101, ResNet-50 and ResNet-18, and compared their effectiveness to identifying AD. To evaluate this approach, an AD Neuroimaging Initiative (ADNI) dataset was utilized. We have also showed uniqueness by using MR images selected only from the central slice containing left and right hippocampus regions to evaluate the models. All the three models used randomly split data in the ratio 7030 for training and testing. Among the three, ResNet-101 showed 98.37% accuracy, better than the other two ResNet models, and performed well in multiclass classification. The promising results emphasize the benefit of using transfer learning specifically when the dataset is low. From this study, we can assure that transfer learning helps to overcome DL problems mainly when the data available is insufficient to train a model from scratch. This approach is highly advantageous in medical image analysis to diagnose diseases like AD.From this study, we can assure that transfer learning helps to overcome DL problems mainly when the data available is insufficient to train a model from scratch. This approach is highly advantageous in medical image analysis to diagnose diseases like AD. Patients with rheumatic diseases are more likely to suffer from anxiety, depression and insomnia. Yet, little is known about mental health status during COVID-19 pandemic. This study aims to measure the prevalence of mental health disorders among patients with rheumatic diseases in the era of COVID-19 pandemic and to determine potential risk factors for major symptoms of depression, anxiety, and insomnia in participants. Participants with rheumatic diseases were asked to complete a questionnaire using a telephonic interview. Small Molecule Compound Library Sociodemographic and rheumatic disease characteristics were recorded. Mental health status was assessed by the patient health questionnaire-9 (PHQ-9), generalized anxiety disorder (GAD)-7, and insomnia severity index (ISI) questionnaires to detect depression, anxiety and insomnia symptoms, respectively. We included 307 patients in the survey. Rheumatoid arthritis was the most frequent diagnosis (55%). Of all participants, 7.5% had known depression and 5.5% known anxiety. Mental health disorders were insomnia (34.