Enhancing brain tumor detection in MRI with a rotation invariant Vision Transformer.

Background: The Rotation Invariant Vision Transformer (RViT) is a novel deep learning model tailored for brain tumor classification using MRI scans.

Methods: RViT incorporates rotated patch embeddings to enhance the accuracy of brain tumor identification.

Results: Evaluation on the Brain Tumor MRI Dataset from Kaggle demonstrates RViT's superior performance with sensitivity (1.

Advanced framework for epilepsy detection through image-based EEG signal analysis.

Background: Recurrent and unpredictable seizures characterize epilepsy, a neurological disorder affecting millions worldwide. Epilepsy diagnosis is crucial for timely treatment and better outcomes. Electroencephalography (EEG) time-series data analysis is essential for epilepsy diagnosis and surveillance.

Automated evaluation of rheumatoid arthritis from hand radiographs using Machine Learning and deep learning techniques.

The aim and objectives of the study are as follows: (i) to implement automated patch-based classification of hand X-ray images using modified pre-trained convolutional neural network (CNN) models; (ii) to develop a customized CNN model for automated feature extraction and classification of hand X-ray images and to compare the performance of customized CNN models with non-linear and linear kernels; (iii) to construct the hand crafted feature fusion (SIFT+ Customized CNN features) and categorize the normal and RA using Machine Learning classifiers. The model was trained on 75 images (10,000 patches) of hand radiographs and tested using 25 images (500 patches) that were not included in the training set. The accuracy of the modified pre-trained model GoogLeNet was 89% and the proposed custom model three achieved an accuracy of 95%.

Deep learning techniques for automated detection of autism spectrum disorder based on thermal imaging.

Children with autism spectrum disorder have impairments in emotional processing which leads to the inability in recognizing facial expressions. Since emotion is a vital criterion for having fine socialisation, it is incredibly important for the autistic children to recognise emotions. In our study, we have chosen the facial skin temperature as a biomarker to measure emotions.

Automated heart sound classification system from unsegmented phonocardiogram (PCG) using deep neural network.

Given the patient to doctor ratio of 50,000:1 in low income and middle-income countries, there is a need for automated heart sound classification system that can screen the Phonocardiogram (PCG) records in real-time. This paper proposes deep neural network architectures such as a one-dimensional convolutional neural network (1D-CNN) and Feed-forward Neural Network (F-NN) for the classification of unsegmented phonocardiogram (PCG) signal. The research paper aims to automate the feature engineering and feature selection process used in the analysis of the PCG signal.

Human Tongue Thermography Could Be a Prognostic Tool for Prescreening the Type II Diabetes Mellitus.

Diabetes mellitus is one of the life threatening diseases over the globe, and an early prediction of diabetes is of utmost importance in this current scenario. International Diabetes Federation (IDF) reported nearly half of the world's population was undiagnosed and unaware of being developed into diabetes. In 2017, around 84 million individuals were living with diabetes, and it might increase to 156 million by the end of 2045 stated by IDF.