Alzheimer’s disease (AD) is an irreversible neurological disorder that affects the vast majority of dementia cases, leading patients to experience gradual memory loss and cognitive function decline. Despite lack a cure, early detection permits provision preventive medication slow disease’s progression. The objective this project develop computer-aided method based on deep learning model distinguish from cognitively normal its stage, mild impairment (MCI), by just using structural MRI (sMRI)....

Background The environment has been significantly impacted by rapid urbanization, leading to a need for changes in climate change and pollution indicators. 4IR offers potential solution efficiently manage these impacts. Smart city ecosystems can provide well-designed, sustainable, safe cities that enable holistic global warming solutions through various community-centred initiatives. These include smart planning techniques, monitoring, governance. An air quality intelligence platform, which...

Epistasis Detection (ED) was widely used for identifying potential risk disease variants in the human genome. A statistically meaningful ED typically requires a more extensive dataset to detect complex disease-associated Single Nucleotide Polymorphisms (SNPs), but single institution generally possesses limited genome data. Thus, it is necessary collect multi-institutional data carry out research together. However, concerns regarding privacy and trustworthiness impede sharing of massive...

Abstract Coronavirus disease of 2019 or COVID-19 is a rapidly spreading viral infection that has affected millions all over the world. With its rapid spread and increasing numbers, it becoming overwhelming for healthcare workers to diagnose condition contain from spreading. Hence become necessity automate diagnostic procedure. This will improve work efficiency as well keep safe getting exposed virus. Medical image analysis one rising research areas can tackle this issue with higher accuracy....

The amalgamation of the Internet medical things with artificial intelligence shows tremendous benefits in health care. Accurate detection fetal QRS complex is highly demanded heart rate monitoring. Detecting using electrophysiological signals obtained from abdominal electrodes seems a promising alternative approach. challenges determining ECG (AECG) require eliminating maternal components and other noises signal at higher accuracy. We propose novel approach an IoT-based deep learning...

The advancement of biomedical research generates myriad healthcare-relevant data, including medical records and device maintenance information. COVID-19 pandemic significantly affects the global mortality rate, creating an enormous demand for devices. As information technology has advanced, concept intelligent healthcare steadily gained prominence. Smart utilises a new generation technologies, such as Internet Things (loT), big cloud computing, artificial intelligence, to completely...

Knee osteoarthritis is one of the most common musculoskeletal diseases and usually diagnosed with medical imaging techniques. Conventionally, case identification using plain radiography practiced. However, we acknowledge that knee a 3D complexity; hence, magnetic resonance will be ideal modality to reveal hidden features from three-dimensional view. In this work, feasibility well-known convolutional neural network (CNN) structures (ResNet, DenseNet, VGG, AlexNet) distinguish knees without...

Gastric cancer is a leading cause of cancer-related deaths worldwide, underscoring the need for early detection to improve patient survival rates. The current clinical gold standard histopathological image analysis, but this process manual, laborious, and time-consuming. As result, there has been growing interest in developing computer-aided diagnosis assist pathologists. Deep learning shown promise regard, each model can only extract limited number features classification. To overcome...

Background Breast cancer remains a pressing global health concern, necessitating accurate diagnostics for effective interventions. Deep learning models (AlexNet, ResNet-50, VGG16, GoogLeNet) show remarkable microcalcification identification (>90%). However, distinct architectures and methodologies pose challenges. We propose an ensemble model, merging unique perspectives, enhancing precision, understanding critical factors breast intervention. Evaluation favors GoogleNet driving their...

The global histogram equalization (HE) has been the most frequently adopted image contrast enhancement technique. A brightness and detail‐preserving HE method with good effect a goal of much recent research in HE. Nevertheless, producing well‐balanced is deemed to be daunting task. In this article, we propose novel framework aim taking three desirable properties into account: preservation, detail enhancement. We termed proposed as multipurpose beta optimized bi‐HE (MBOBHE). MBOBHE consists...

Chest X-ray (CXR) interpretations are conducted in hospitals and medical facilities on daily basis. If the interpretation tasks were performed correctly, various vital conditions of patients can be revealed such as pneumonia, pneumothorax, interstitial lung disease, heart failure bone fracture. The current practices often involve tedious manual processes dependent expertise radiologist or consultant, thus, execution is easily prone to human errors being misdiagnosed. With recent advances...

Objective Deep learning algorithms have long been involved in the diagnosis of severe neurological disorders that interfere with patients’ everyday tasks, such as Parkinson’s disease (PD). The most effective imaging modality for detecting condition is DaTscan, a variety single-photon emission computerized tomography (SPECT) method. goal to create convolutional neural network can specifically identify region interest following feature extraction. Methods study comprised total 1,390 DaTscan...

Computer-aided Invasive Ductal Carcinoma (IDC) grading classification systems based on deep learning have shown that may achieve reliable accuracy in IDC grade using histopathology images. However, there is a dearth of comprehensive performance comparisons Convolutional Neural Network (CNN) designs the literature. As such, we would like to conduct comparison analysis seven selected CNN models: EfficientNetB0, EfficientNetV2B0, EfficientNetV2B0-21k, ResNetV1-50, ResNetV2-50, MobileNetV1, and...