A5072731605- Brain Tumor Detection and Classification
- AI in cancer detection
- Advanced Neural Network Applications
- Radiomics and Machine Learning in Medical Imaging
- Data Mining Algorithms and Applications
- Cloud Computing and Resource Management
- IoT and Edge/Fog Computing
- Advanced Clustering Algorithms Research
- Ethics and Social Impacts of AI
- Privacy, Security, and Data Protection
- COVID-19 Digital Contact Tracing
- Medical Image Segmentation Techniques
- Artificial Intelligence in Healthcare and Education
- Blockchain Technology Applications and Security
- Caching and Content Delivery
- Data Management and Algorithms
- Speech and Audio Processing
- Explainable Artificial Intelligence (XAI)
- Privacy-Preserving Technologies in Data
- Music and Audio Processing
- Adversarial Robustness in Machine Learning
- Image Retrieval and Classification Techniques
- Digital Media Forensic Detection
- COVID-19 diagnosis using AI
- Software System Performance and Reliability
Ollscoil na Gaillimhe – University of Galway
2022-2025
Dublin City University
2019-2022
Science Foundation Ireland
2021-2022
Irish Research Council
2021-2022
Software (Spain)
2022
Lero
2021-2022
Universidade de Pernambuco
2020
Trinity College Dublin
2020
Weatherford College
2019
University College Dublin
2015-2018
Abstract Brain tumor localization and segmentation from magnetic resonance imaging (MRI) are hard important tasks for several applications in the field of medical analysis. As each brain modality gives unique key details related to part tumor, many recent approaches used four modalities T1, T1c, T2, FLAIR. Although them obtained a promising result on BRATS 2018 dataset, they suffer complex structure that needs more time train test. So, this paper, obtain flexible effective system, first, we...
This study utilizes the Breast Ultrasound Image (BUSI) dataset to present a deep learning technique for breast tumor segmentation based on modified UNet architecture. To improve accuracy, model integrates attention mechanisms, such as Convolutional Block Attention Module (CBAM) and Non-Local Attention, with advanced encoder architectures, including ResNet, DenseNet, EfficientNet. These mechanisms enable focus more effectively relevant areas, resulting in significant performance improvements....
The fourth industrial revolution heralds a paradigm shift in how people, processes, things, data and networks communicate connect with each other. Conventional computing infrastructures are struggling to satisfy dramatic growth demand from deluge of connected heterogeneous end points located at the edge while, same time, meeting quality service levels. complexity makes it increasingly difficult for infrastructure providers plan provision resources meet this demand. While simulation...
The COVID-19 pandemic is a global, national, and local public health concern which has caused significant outbreak in all countries regions for both males females around the world. Automated detection of lung infections their boundaries from medical images offers great potential to augment patient treatment healthcare strategies tackling its impacts. Detecting this disease CT scan perhaps one fastest ways diagnose patients. However, finding presence infected tissues segment them slices faces...
The ongoing development of audio datasets for numerous languages has spurred research activities towards designing smart speech recognition systems. A typical system can be applied in many emerging applications, such as smartphone dialing, airline reservations, and automatic wheelchairs, among others. Urdu is a national language Pakistan also widely spoken other South Asian countries (e.g., India, Afghanistan). Therefore, we present comprehensive dataset digits ranging from 0 to 9. Our...
Abstract The increased intracranial pressure (ICP) can be described as an increase in around the brain and lead to serious health problems. assessment of ultrasound images is commonly conducted by skilled experts which a time-consuming approach, but advanced computer-aided diagnosis (CAD) systems assist physician decrease time ICP diagnosis. accurate detection nerve optic regions, with drawing precise slope line behind eyeball calculating diameter optic, are main aims this research. First,...
Deep Learning (DL) algorithms have shown impressive performance in diverse domains. Among them, audio has attracted many researchers over the last couple of decades due to some interesting patterns–particularly classification data. For better classification, feature selection and combination play a key role as they potential make or break any DL model. To investigate this role, we conduct an extensive evaluation several cutting-edge models (i.e., Convolutional Neural Network, EfficientNet,...
One of the leading algorithms and architectures in deep learning is Convolution Neural Network (CNN). It represents a unique method for image processing, object detection, classification. CNN has shown to be an efficient approach machine computer vision fields. composed several filters accompanied by nonlinear functions pooling layers. enforces limitations on weights interconnections neural network create good structure processing spatial temporal distributed data. A can restrain numbering...
New blockchain platforms are launching at a high cadence, each fighting for attention, adoption, and infrastructure resources. Several studies have measured the peer-to-peer (P2P) network decentralisation of Bitcoin Ethereum (i.e., two largest used platforms). However, with increasing demand infrastructure, it is important to study node across multiple networks, especially those containing small number nodes. In this paper, we propose NodeMaps, data processing framework capture, analyse,...
Acute Lymphoblastic Leukemia (ALL) is a life-threatening malignancy characterized by its aggressive progression and detrimental effects on the hematopoietic system. Early accurate diagnosis paramount to optimizing therapeutic interventions improving clinical outcomes. This study introduces novel diagnostic framework that synergizes EfficientNet-B7 architecture with Explainable Artificial Intelligence (XAI) methodologies address challenges in performance, computational efficiency,...
Abstract The importance of gastric cancer (GC) and the role deep learning techniques in categorizing GC histopathology images have recently increased. Identifying drawbacks traditional models, including lack interpretability, inability to capture complex patterns, adaptability, sensitivity noise. A multi-channel attention mechanism-based framework is proposed that can overcome limitations conventional models by dynamically focusing on relevant features, enhancing extraction, capturing...
The present study is developed a new approach using computer diagnostic method to diagnosing diabetic diseases with the use of fluorescein images. In doing so, this presented growth region algorithm for aim diabetes, considering angiography images patients’ eyes. addition, integrated two methods, including fuzzy C-means (FCM) and genetic (GA) predict retinopathy in patients from was applied total 224 As clearly confirmed by obtained results, GA-FCM outperformed hand regarding selection...