Browsing by Author "Galal, Sabaa Ahmed Yahya"

The substantial progress of medical imaging technology in the last decade makes it challenging for medical experts and radiologists to analyse and classify them. Medical images contain massive information that can be used for diagnosis, surgical planning, training, and research. The ability to estimate conclusions without direct human input in healthcare systems using computer algorithms is known as Artificial intelligence (AI) in healthcare. Deep Learning (DL) approaches are already being employed or exploited for healthcare purposes. There is, therefore, a need for a technique that can automatically analyze and classify the images based on their respective contents. DL algorithms open a world of opportunities, and it has been recently used for medical images analysis. Although DL techniques have demonstrated a breakthrough in medical images analysis, research still ongoing to improve the accuracy rate. This research focuses on DL in the context of analysing Magnetic Resonance Imaging (MRI) brain medical images. A comprehensive review of the state-of-the-art processing of brain medical images using DL is conducted in this research. The scope of this research is restricted to three digital databases: (1) the Science Direct database, (2) the IEEEXplore Library of Engineering and Technology Technical Literature, and (3) Scopus database. More than 400 publications were evaluated and discussed in this research. The research focus on both binary classification and multi-class classification. For binary classification, the dataset used is from the brain tumor classification project which contains tumorous and non-tumorous images, and it is available for research and development. For multi-class classification, the dataset contains T1-weighted contrast-enhanced MRI medical images from 233 patients with three types of tumours: meningioma, glioma, and pituitary which is also available for research and development. The proposed neural model is fully automatic brain tumour MRI medical images classification model that uses Convolutional Neural Network (BTMIC-CNN). The model's excellent performance was confirmed using the evaluation metrics and reported a total accuracy of 99%. It outperforms existing methods in terms of classification accuracy and is expected to help radiologists and doctors accurately classify brain tumours’ images. This study contributes to goal 3 of the Sustainable Development Goals (SDGs), which involves excellent health and well-being.

Relaxation and calmness are two emotions that people continually seek. One popular method people frequently used to reduce their tension and pressure levels is listening to various types of relaxing music. However, the Quran is composed of Allah’s words, which were ultimately given for the benefit of humanity. Muslims strongly believe that listening to or reading the Quran brings them comfort, pleasure and confidence that would otherwise elude them; however, scientific evidence is still required to prove that this belief has a scientific basis. Recently, researchers have used electrophysiology to explore the relationships between electrical phenomena and body processes. This research aims to study and analyse the electrical activity of people`s brains and hearts when listening to Quranic recitation compared with listening to relaxing music. Two types of electrophysiology readings are used in this research: electroencephalograms (EEGs) and electrocardiograms (ECGs). An EEG measures brain electrical activity, and an ECG measures heart electrical activity. EEG and ECG data were collected from twenty-five subjects. Then, machine learning algorithms were applied to the EEG and ECG signals. In addition, EEG brainwaves were measured, focusing on the alpha and beta bands. The ECG signal analysis also involved heart rate calculation. All these types of analysis were used to measure subjects’ calmness levels and to recognize their emotions while listening to Quranic recitation as compared with listening to relaxing music. With respect to the valence-arousal analysis result, we conclude that Quranic recitation demonstrated a positive transformation of the subjects` emotions: from negative precursor emotions to calmness and happiness conditions denoted by a positive valence for the EEG and ECG signals. In contrast, relaxing music showed a positive transformation with regard to the valence in the EEG analysis; however, with respect to the ECG music data analysis, the results revealed a negative transformation for most of the music tracks.