One of the top causes of mortality in people globally is a brain tumor. Today, biopsy is regarded as the cornerstone of cancer diagnosis. However, it faces difficulties, including low sensitivity, hazards during biopsy treatment, and a protracted waiting period for findings. In this context, developing non-invasive and computational methods for identifying and treating brain cancers is crucial. The classification of tumors obtained from an MRI is crucial for making a variety of medical diagnoses. However, MRI analysis typically requires much time. The primary challenge is that the tissues of the brain are comparable. Numerous scientists have created new techniques for identifying and categorizing cancers. However, due to their limitations, the majority of them eventually fail. In that context, this work presents a novel way of classifying multiple types of brain tumors. This work also introduces a segmentation algorithm known as Canny Mayfly. Enhanced chimpanzee optimization algorithm (EChOA) is used to select the features by minimizing the dimension of the retrieved features. ResNet-152 and the softmax classifier are then used to perform the feature classification process. Python is used to carry out the proposed method on the Figshare dataset. The accuracy, specificity, and sensitivity of the proposed cancer classification system are just a few of the characteristics that are used to evaluate its overall performance. According to the final evaluation results, our proposed strategy outperformed, with an accuracy of 98.85%.

Objective: This study aimed to determine the prevalence of knee osteoarthritis (OA) in Saudi Arabia and the association between knee OA and modifiable and non-modifiable risk factors. Methods: A self-reported, population-based, cross-sectional survey between January 2021 and October 2021 was conducted. A large, population-representative sample (n = 2254) of adult subjects aged 18 years and over from all regions of Saudi Arabia was collected electronically using convenience sampling. The American College of Rheumatology (ACR) clinical criteria were used to diagnose OA of the knee. The knee injury and osteoarthritis outcome score (KOOS) was used to investigate the severity of knee OA. This study focused on modifiable risk factors (body mass index, education, employment status, marital status, smoking status, type of work, previous history of knee injury, and physical activity level) and non-modifiable risk factors (age, gender, family history of OA, and presence of flatfoot). Results: The overall prevalence of knee OA was 18.9% (n = 425), and women suffered more compared to their male counterparts (20.3% vs. 13.1%, p = 0.001). The logistic regression analysis model showed age (OR: 1.06 [95% CI: 1.05–1.07]; p < 0.01), sex (OR: 2.14 [95% CI: 1.48–3.11]; p < 0.01), previous injury (OR: 3.95 [95% CI: 2.81–5.56]; p < 0.01), and obesity (OR: 1.07 [95% CI: 1.04–1.09]; p < 0.01) to be associated with knee OA. Conclusions: A high prevalence of knee OA underlines the need for health promotion and prevention programmes that focus on modifiable risk factors to decrease the burden of the problem and the cost of treatment in Saudi Arabia.

Intracranial hemorrhage (ICH) is a serious medical condition that necessitates a prompt and exhaustive medical diagnosis. This paper presents a multi-label ICH classification issue with six different types of hemorrhages, namely epidural (EPD), intraparenchymal (ITP), intraventricular (ITV), subarachnoid (SBC), subdural (SBD), and Some. A patient may experience numerous hemorrhages at the same time in some situations. A CT scan of a patient’s skull is used to detect and classify the type of ICH hemorrhage(s) present. First, our model determines whether there is a hemorrhage or not; if there is a hemorrhage, the model attempts to identify the type of hemorrhage(s). In this paper, we present a hybrid deep learning approach that combines convolutional neural network (CNN) and Long-Short Term Memory (LSTM) approaches (Conv-LSTM). In addition, to propose viable solutions for the problem, we used a Systematic Windowing technique with a Conv-LSTM. To ensure the efficacy of the proposed model, experiments are conducted on the RSNA dataset. The suggested model provides higher sensitivity (93.87%), specificity (96.45%), precision (95.21%), and accuracy (95.14%). In addition, the obtained F1 score results outperform existing deep neural network-based algorithms.

The largest source of information in healthcare during the present epidemic is radiological imaging, which is also one of the most difficult sources to interpret. Clinicians today are forced to rely heavily on therapeutic image analysis that has been filtered and sometimes performed by worn-out radiologists. Transmission of these medical data increases in frequency due to patient overflow, and protecting confidentiality, along with integrity and availability, emerges as one of the most crucial components of security. Medical images generally contain sensitive information about patients and are therefore vulnerable to various security threats during transmission over public networks. These images must be protected before being transmitted over this network to the public. In this paper, an efficient SCAN and chaotic-map-based image encryption model is proposed. This paper describes pixel value and pixel position manipulation based on SCAN and chaotic theory. The SCAN method involves translating an image’s pixel value to a different pixel value and rearranging pixels in a predetermined order. A chaotic map is used to shift the positions of the pixels within the block. Decryption follows the reverse process of encryption. The effectiveness of the suggested strategy is evaluated by computing the histogram chi-square test, MSE, PSNR, NPCR, UACI, SSIM, and UQI. The efficiency of the suggested strategy is demonstrated by comparison analysis. The results of analysis and testing show that the proposed program can achieve the concept of partial encryption. In addition, simulation experiments demonstrate that our approach has both a faster encryption speed and higher security when compared to existing techniques.

Since the workplace has become desk-based and leisure time has become dominated by digital relaxation modes, the world is moving at a pace where physical activity has become a time-bound routine. The negative effects of extended sitting are a global concern since the workforce is becoming more desk based. There is a dearth of reviews that may link the knowledge on the effects of sedentary behaviour on hypertension and its accompanying damage to the brain and blood vessels and provide a future scope for the investigations connected to the relationship between sedentary behaviour and hypertension. Methods: Based on the database search and extensive research we did, we found studies that concentrated on the adverse effects of sedentary behaviour in association with blood pressure, cognitive decline and brain damage on adults. Results: We extracted 12 articles out of 20,625. We identified the potential adverse effects of sedentary behaviour, methods to reduce sedentary behaviour and the positive changes on health due to the interventions introduced. Sedentary lifestyle has shown a decline in human health. However, the visible symptoms presented later in life makes it very important to know the areas of decline and look for ways to curb the decline or procrastinate it.