Computational Biology & Bioinformatics

Abstract

This research focuses on the advanced detection and classification of brain tumours using state-of-the-art deep learning models. A dataset from the NHS Hospital in Jalandhar, Punjab, which contains several MRI images of brain tumours with segmented locations, is used in this work. The first steps in pre-processing are to transform colour photos to grayscale and remove unnecessary components, such as patient data. Tumours are first detected and segmented using the YOLOv8 model, which has been trained with correctly structured images and labels. Subsequently, five types of tumours were taken into consideration for classification: lesion, calcification, infarct, bleed and microbleed. For classification of brain tumours, the U-net model was employed. The newly developed YOLOv8 model effectively detected brain tumours with an 83% precision rate, while the U-net model was near-perfect, classifying tumours with 99% accuracy. Finding precise tumour placements inside and classifying them is the main objective since it will provide light on how tumour locations affect the cognitive abilities of the brain. This study will make a major contribution to the field of brain tumour research by enhancing the precision of diagnosis and the approaches used for treatment.

Abstract

Alzheimer’s disease is a degenerative disorder of the brain that affects memory and cognitive function, and is becoming more prevalent as the population ages. Alzheimer causes brain cells of a person to die, and with time the brain works less. As a result of this there is a change in the behavior and personality of Alzheimer patients. It is observed that the patients very often suffer from fluctuating mood. Due to the change in mood of the patient, the caretaker or the attendant of the patient is unable to distinguish what triggered the changes, and provide proactive support or timely intervention. Hence the study is undertaken to detect the mood of the Alzheimer’s patient employing the voice analysis, computer vision and deep learning. This paper comprises of two phases. The first phase detects the emotions of the patients in real time with the help of computer vision (CV), voice analysis (VA) and Convolutional Neural Network (CNN). Two CNN models were trained, first with the attributes extracted from the image and second the features extracted from the voice dataset. Then the predictions are compared to get the result from the proposed model. The second phase of the paper examines the practicality of the proposed approach by applying it to detect the emotions in four Alzheimer’s patients. Finally, the results are compared and validated. Overall, the proposed model holds promise as a valuable tool in the real time detection of emotions in Alzheimer patient, enabling timely intervention and improved patient outcomes.

Abstract

This research explores an intelligent application designed to support music therapy for children with autism using advanced deep learning. Built with the Tkinter Python library as a web-based platform, it targets seven core therapeutic objectives. A central feature is personalized treatment planning via classification neural networks, tailored from each child’s therapeutic history, usage patterns, and longitudinal trends. Advanced audio processing and machine learning aim to adapt therapy to individual needs. An intuitive interface supports accessibility and engagement, while real-time feedback enables ongoing adjustments to interventions. The work addresses the distinct needs of autistic children in a supportive, adaptive therapeutic setting; preliminary directions suggest stronger engagement and emotional-response signals when therapeutic plans can be refined using live analytics.

Abstract

The diagnosis of mental illnesses and their subcategories has long been subject to the clinician’s assessment and lacks an objective measurement criteria, leading to misdiagnosis and poor patient acceptance rates of their illness. For patients with bipolar disorder, the anosognosia rate stands at 40%; thus, they chronically refuse having an illness and avoid taking medication. Also, family members and those around these individuals are unable to objectively identify the impact of the illness on the patient, and this leads to poor social relationships and stigmatization of BD patients. Moreover, the functioning of BD patients is not efficient, as they do not have a proper way of tracking their illness. The intervention designed is a device prototype along with a mobile app aimed at using the non-invasive biomarkers of speech, heart rate, sleep and physical activity to objectively classify the affective states of Bipolar Disorder, and consequently communicate results to patients to provide guidance on the right course of action using evidence-based Cognitive Behavioral Therapy. The research was conducted in two parts. The first part was the assembly of components for the wearable electronic device that would monitor the symptoms and detect the phase.