Monkeypox is a zoonotic viral disease caused by the monkeypox virus. After its recent outbreak, it has become clear that rapid, accurate, and reliable diagnosis may help reduce risk of future outbreak. The presence skin lesions one most prominent symptoms disease. However, this symptom also peculiar to chickenpox. resemblance in human subject disrupt effective and, as result, lead misdiagnosis. Such misdiagnosis can further spread communicable eventually result an As deep learning (DL)...

On average, breast cancer kills one woman per minute. However, there are more reasons for optimism than ever before. When diagnosed early, patients with have a better chance of survival. This study aims to employ novel approach that combines artificial intelligence and multi-criteria decision-making method robust evaluation machine learning models. The proposed techniques comprise various supervised algorithms, while the technique implemented includes Preference Ranking Organization Method...

Due to its high prevalence and incidence, diabetes is considered significant public health. Since has no known cure, early diagnosis plays a vital role in effectively managing the disease. Feature scaling step pre-processing data before building model using machine learning. The datasets used for training learning often contain unpredictable values that may have varying scales. This can result inequalities comparing these values. techniques address challenges by adjusting promoting easy fair...

The brain is an intrinsic and complicated component of human anatomy. It a collection connective tissues nerve cells that regulate the principal actions entire body. Brain tumor cancer serious mortality factor highly intractable disease. Even though tumors are not considered fundamental cause deaths worldwide, about 40% other types metastasized to transform into tumors. Computer-aided devices for diagnosis through magnetic resonance imaging (MRI) have remained gold standard tumors, but this...

Disease prediction is greatly challenged by the scarcity of datasets and privacy concerns associated with real medical data. An approach that stands out to circumvent this hurdle use synthetic data generated using Generative Adversarial Networks (GANs). GANs can increase volume while generating have no direct link personal information. This study pioneers create augmented traditional augmentation techniques for our binary classification task. The primary aim research was evaluate performance...

Abstract The scarcity of medical imaging datasets and privacy concerns pose significant challenges in artificial intelligence-based disease prediction. This poses major to patient confidentiality as there are now tools capable extracting information by merely analysing patient’s data. To address this, we propose the use synthetic data generated generative adversarial networks a solution. Our study pioneers utilisation novel Pix2Pix network model, specifically ‘image-to-image translation with...

Malaria is a significant health concern in many third-world countries, especially for pregnant women and young children. It accounted about 229 million cases 600,000 mortality globally 2019. Hence, rapid accurate detection vital. This study focused on achieving three goals. The first to develop deep learning framework capable of automating accurately classifying malaria parasites using microscopic images thin thick peripheral blood smears. second report which the two smears most appropriate...

Acute Myeloid Leukemia (AML) is a complex hematologic malignancy where precise subtype classification critical for targeted treatment and improved patient outcomes. This study explores the potential of ConvNeXt, an advanced convolutional neural network architecture, high-resolution peripheral blood smear image into AML subtypes. The dataset from specialized hematopathology center provides diverse representative sample, addressing gaps in global leukemia diagnostics. A comprehensive deep...

(1) Background: Acute Myeloid Leukemia (AML) is a complex hematologic malignancy where accurate subtype classification crucial for targeted treatment and improved patient outcomes. Automated AML detection especially important underrepresented subtypes to ensure equitable diagnostics; (2) Methods: This study explores the potential of ConvNeXt, an advanced convolutional neural network architecture, classifying high-resolution peripheral blood smear images into subtypes. A deep learning...

Prostate cancer is a leading cause of cancer-related morbidity and mortality worldwide, with diagnostic challenges magnified in underrepresented regions like sub-Saharan Africa. This study introduces novel application ConvNeXt, an advanced convolutional neural network architecture, for multi-class classification prostate histopathological images into normal, benign, malignant categories. The dataset, sourced from tertiary healthcare institution Nigeria, represents typically underserved...

Tuberculosis, a deadly and contagious disease caused by Mycobacterium tuberculosis, remains significant global public health threat. HIV co-infection significantly increases the risk of active TB recurrence prolongs medical treatment for tuberculosis (TB). The study focuses on using advanced machine learning (ML) techniques to predict incidence HIV-TB data from 2023 World Health Organization (WHO) Global burden database. estimated rate all types per 100,000 people (E_inc_100k) HIV-positive...

Cryptococcus neoformans is an opportunistic fungal pathogen with significant medical importance, especially in immunosuppressed patients. It the causative agent of cryptococcosis. An estimated 220,000 annual cases cryptococcal meningitis (CM) occur among people HIV/AIDS globally, resulting nearly 181,000 deaths. The gold standards for diagnosis are either direct microscopic identification or cultures. However, these diagnostic methods need special types equipment and clinical expertise,...

Abstract This study explored the application of machine learning in predicting post-treatment outcomes for chronic neck pain patients undergoing a multimodal program featuring cervical extension traction (CET). Pre-treatment demographic and clinical variables were used to develop predictive models capable anticipating modifications lordotic angle (CLA), disability 570 treated between 2014 2020. Linear regression pre-treatment age, body mass index, CLA, anterior head translation, score,...

Skin lesion detection is crucial in diagnosing and managing dermatological conditions. In this study, we developed demonstrated the potential applicability of a novel mixed-scale dense convolution, self-attention mechanism, hierarchical feature fusion, attention-based contextual information technique (MSHA) model for skin using digital images chickenpox shingles lesions. The adopts combination unique architectural designs, such as convolution layer, information, enabling MSHA to capture...

Cancer is a disease with rare, diverse symptoms, causing abnormal cell growth in an uncontrolled way, leading to damage, apoptosis, and eventually death of the patient. This study uses Fuzzy PROMETHEE technique develop new path for cancer treatment based on nanoparticles (NPs) applications, used controlled anticancer drug delivery (drug release, toxicity, unspecific site targeting) enhance patient safety. The different employed analysis are gold (AuNPs), liposomes, dendrimers, polymeric...

Background: In planning radiotherapy treatments, computed tomography (CT) has become a crucial tool. CT scans involve exposure to ionizing radiation, which can increase the risk of cancer and other adverse health effects in patients. Ionizing radiation doses for medical must be kept “As Low As Reasonably Achievable”. Very few articles on guidelines radiotherapy-computed are available. This paper reviews current literature dose optimization based effective diagnostic reference level (DRL)...

Numerical electronic health records often come with numerous features and outliers. These are usually indicators of medical diseases. To prevent poor model performance associated the curse dimensionality, these must be reduced only most important ones retained. Also, outliers may result in generalization a machine learning model. This study investigates impact dimensionality reduction on models for disease diagnosis. After fitting datasets to models, was evaluated using evaluation metrics....

Thyroid cancer is the most common endocrine malignancy associated with follicular or non-follicular thyroid cells. While ionizing radiation a significant risk factor for cancer, other variables like smoking, obesity, hormone exposure, and environmental pollutants also contribute. This study aims to evaluate current treatment alternatives find effective ones. will assist clinicians patient families in selecting option negligible adverse side effects. The employed technique of fuzzy-based...

Aim: Autism spectrum disorder is a class of neurological disorders that affect the development brain functions. This study aims to evaluate, compare and rank therapy techniques used in management autism using multicriteria decision-making approaches. Materials & methods: Fuzzy PROMETHEE fuzzy TOPSIS approaches were used. utilizes pair-wise comparison alternatives under environment while geometric distance from positive ideal solution for evaluation effectiveness alternatives.The selected...