A hybrid explainable model based on advanced machine learning and deep learning models for classifying brain tumors using MRI images.

Brain tumors present a significant global health challenge, and their early detection and accurate classification are crucial for effective treatment strategies. This study presents a novel approach combining a lightweight parallel depthwise separable convolutional neural network (PDSCNN) and a hybrid ridge regression extreme learning machine (RRELM) for accurately classifying four types of brain tumors (glioma, meningioma, no tumor, and pituitary) based on MRI images. The proposed approach enhances the visibility and clarity of tumor features in MRI images by employing contrast-limited adaptive histogram equalization (CLAHE).

Interpretable deep learning architecture for gastrointestinal disease detection: A Tri-stage approach with PCA and XAI.

GI abnormalities significantly increase mortality rates and impose considerable strain on healthcare systems, underscoring the essential requirement for rapid detection, precise diagnosis, and efficient strategic treatment. To develop a CAD system, this study aims to automatically classify GI disorders utilizing various deep learning methodologies. The proposed system features a three-stage lightweight architecture, consisting of a feature extractor using PSE-CNN, a feature selector employing PCA, and a classifier based on DELM.

Endoscopic retrograde cholangiopancreatography training conditions, results from a pan-European survey: Between vision and reality.

Background: Endoscopic retrograde cholangiopancreatography (ERCP) still has a relatively high complication rate, underscoring the importance of high-quality training. Despite existing guidelines, real-world data on training conditions remain limited. This pan-European survey aims to systematically explore the perceptions surrounding ERCP training.

CSXAI: a lightweight 2D CNN-SVM model for detection and classification of various crop diseases with explainable AI visualization.

Plant diseases significantly impact crop productivity and quality, posing a serious threat to global agriculture. The process of identifying and categorizing these diseases is often time-consuming and prone to errors. This research addresses this issue by employing a convolutional neural network and support vector machine (CNN-SVM) hybrid model to classify diseases in four economically important crops: strawberries, peaches, cherries, and soybeans.

A new encryption algorithm for image data based on two-way chaotic maps and iterative cellular automata.

Due to their simplicity of implementation and compliance with the encryption issue, chaotic models are often utilized in picture encryption applications. Despite having many benefits, this approach still has a crucial space issue that makes encryption algorithms based on it susceptible to brute-force assaults. This research's proposed novel picture encryption technique has a vast key space and great key sensitivity.

Pernicious anemia presenting with dysphagia and melanoderma: a confusing manifestation.

Vitamin B12 deficiency is widely recognized as a common cause of anemia. However, symptoms such as dysphagia, melanoderma, and pancytopenia, although less frequent, can also be associated with this deficiency. We report the case of a 47-year-old Caucasian man presented with dysphagia to solids associated to high heart rate, dyspnea and melanoderma.

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Introduction: Vitamin D plays a role in regulating the immune response through an immunomodulatory effect, and is probably involved in the pathophysiology of Crohn's disease (CD).

Aim: to assess vitamin D status in patients with CD as well as in healthy controls and to determine associated factors of vitamin D deficiency in patients with CD.

Methods: We conducted a prospective study over 18 months, including CD patients with age and sex matched with healthy controls.

Differences in Physical Activity, Sedentary Behavior, Health-Related Physical Performance Indices and Academic Achievement: A Comparative Study of Normal-Weight and Obese Children in Qatar.

The relationship between physical activity (PA), health-related physical performance (PP), and academic achievement (AA) plays an important role in childhood. This study examined the differences in PA, sedentary behavior, health-related PP, maturity status, and AA between normal-weight and obese school children in Qatar. Eighty schoolchildren were recruited (age: 12.

Prevalence and prognosis of Computed-Tomography defined sarcopenia in Tunisian cirrhotic patients.

Introduction: Sarcopenia is an underdiagnosed and understudied complication of cirrhosis, especially in patients not undergoing liver transplantation.

Aim: To evaluate the prevalence and prognostic impact of radiological sarcopenia in non-transplanted cirrhotic patients.

Methods: Longitudinal retrospective study including cirrhotic patients explored by an abdominal CT scan, over a period of 6 years, in a single gastroenterology department in Tunisia.

Deep Learning Framework for Liver Segmentation from -Weighted MRI Images.

The human liver exhibits variable characteristics and anatomical information, which is often ambiguous in radiological images. Machine learning can be of great assistance in automatically segmenting the liver in radiological images, which can be further processed for computer-aided diagnosis. Magnetic resonance imaging (MRI) is preferred by clinicians for liver pathology diagnosis over volumetric abdominal computerized tomography (CT) scans, due to their superior representation of soft tissues.

Western outcomes of circumferential endoscopic submucosal dissection for early esophageal squamous cell carcinoma.

Background And Aims: Circumferential endoscopic submucosal dissection (cESD) in the esophagus has been reported to be feasible in small Eastern case series. We assessed the outcomes of cESD in the treatment of early esophageal squamous cell carcinoma (ESCC) in Western countries.

Methods: We conducted an international study at 25 referral centers in Europe and Australia using prospective databases.

Mesenteric Arterial Thrombosis Revealing Relapse of Nephrotic Syndrome in Young Women.

Background: Nephrotic syndrome (NS) is associated with a hypercoagulable state and may be complicated by thrombotic events. Venous thrombosis is well-acknowledged, while arterial thrombosis is rather unusual.

Case Presentation: We present the case of a 20-year-old woman with a 12-year history of idiopathic NS revealed by extensive cerebral venous thrombosis with pulmonary embolism treated with anticoagulation therapy and oral corticosteroid therapy followed by mycophenolate mofetil (MMF).

IRv2-Net: A Deep Learning Framework for Enhanced Polyp Segmentation Performance Integrating InceptionResNetV2 and UNet Architecture with Test Time Augmentation Techniques.

Colorectal polyps in the colon or rectum are precancerous growths that can lead to a more severe disease called colorectal cancer. Accurate segmentation of polyps using medical imaging data is essential for effective diagnosis. However, manual segmentation by endoscopists can be time-consuming, error-prone, and expensive, leading to a high rate of missed anomalies.

Iatrogenic orbital hematoma: an exceptional complication of upper gastrointestinal endoscopy.

Iatrogenic orbital hematoma is a rare event, most often manifested by a painful exophthalmos that could compromise the patient's visual prognosis. We report the case of a 51-year-old female patient with a history of non-ischemic dilated cardiomyopathy and high blood pressure, who developed a painful exophthalmos following an upper gastrointestinal sedation-free endoscopy. The diagnosis of an intra-orbital hematoma was made by computed tomographic scan and a conservative attitude was adopted after a thorough ophthalmological examination.

Signer-Independent Arabic Sign Language Recognition System Using Deep Learning Model.

Every one of us has a unique manner of communicating to explore the world, and such communication helps to interpret life. Sign language is the popular language of communication for hearing and speech-disabled people. When a sign language user interacts with a non-sign language user, it becomes difficult for a signer to express themselves to another person.

Application of Green Polymeric Nanocomposites for Enhanced Oil Recovery by Spontaneous Imbibition from Carbonate Reservoirs.

This study experimentally investigates the effect of green polymeric nanoparticles on the interfacial tension (IFT) and wettability of carbonate reservoirs to effectively change the enhanced oil recovery (EOR) parameters. This experimental study compares the performance of xanthan/magnetite/SiO nanocomposites (NC) and several green materials, i.e.

Non-ANCA Leucocytoclasic Vasculitis Induced by Sofosbuvir/Ledipasvir Treatment: A Case Report.

Background: The development of direct-acting antivirals directed against the Hepatitis C Virus has dramatically modified the therapeutic approach to chronic hepatic viral disease. Larger use of such drugs has also led to increasing reports about their adverse effects. This report aimed to describe a case of leucocytoclasic vasculitis following treatment based on the sofosbuvir/ledipasvir regimen with complete disappearance shortly after withdrawal in a 61-year-old patient treated for genotype 1 hepatitis C.

Design and Performance Evaluation of a Novel Spiral Head-Stem Trunnion for Hip Implants Using Finite Element Analysis.

With an expectation of an increased number of revision surgeries and patients receiving orthopedic implants in the coming years, the focus of joint replacement research needs to be on improving the mechanical properties of implants. Head-stem trunnion fixation provides superior load support and implant stability. Fretting wear is formed at the trunnion because of the dynamic load activities of patients, and this eventually causes the total hip implant system to fail.

A Lightweight Deep Learning Based Microwave Brain Image Network Model for Brain Tumor Classification Using Reconstructed Microwave Brain (RMB) Images.

Computerized brain tumor classification from the reconstructed microwave brain (RMB) images is important for the examination and observation of the development of brain disease. In this paper, an eight-layered lightweight classifier model called microwave brain image network (MBINet) using a self-organized operational neural network (Self-ONN) is proposed to classify the reconstructed microwave brain (RMB) images into six classes. Initially, an experimental antenna sensor-based microwave brain imaging (SMBI) system was implemented, and RMB images were collected to create an image dataset.

Constructing an Intelligent Model Based on Support Vector Regression to Simulate the Solubility of Drugs in Polymeric Media.

This study constructs a machine learning method to simultaneously analyze the thermodynamic behavior of many polymer-drug systems. The solubility temperature of Acetaminophen, Celecoxib, Chloramphenicol, D-Mannitol, Felodipine, Ibuprofen, Ibuprofen Sodium, Indomethacin, Itraconazole, Naproxen, Nifedipine, Paracetamol, Sulfadiazine, Sulfadimidine, Sulfamerazine, and Sulfathiazole in 1,3-bis[2-pyrrolidone-1-yl] butane, Polyvinyl Acetate, Polyvinylpyrrolidone (PVP), PVP K12, PVP K15, PVP K17, PVP K25, PVP/VA, PVP/VA 335, PVP/VA 535, PVP/VA 635, PVP/VA 735, Soluplus analyzes from a modeling perspective. The least-squares support vector regression (LS-SVR) designs to approximate the solubility temperature of drugs in polymers from polymer and drug types and drug loading in polymers.