One of the primary challenges leading to a significant reduction in agricultural production is the prevalence of diseases affecting citrus plants. Prevention and monitoring the spread of citrus plant diseases is crucial for maintaining citrus product...
Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) are essential clinical cross-sectional imaging techniques for diagnosing complex conditions. However, large 3D datasets with annotations for deep learning are scarce. While methods like DI...
Early and accurate detection of oral cancer plays a pivotal role in improving patient outcomes. This research introduces a custom-designed, 19-layer convolutional neural network (CNN) for the automated diagnosis of oral cancer using clinical images o...
The absence of reliable early treatment serves as one of the main causes of cervical cancer. Hence, it is crucial to detect cervical cancer early. The biggest challenge in diagnosing cervical cancer early is that it is asymptomatic until it develops ...
Accurate prediction of drug side effect frequencies is critical for drug safety assessment but remains challenging due to the high cost of clinical trials and the limited generalizability of existing models. We propose Multi Fingerprint and Graph Emb...
The foveola, the central region of the human retina, plays a crucial role in sharp color vision and is challenging to study due to its unique anatomy and technical limitations in imaging. We present ConeMapper, an open-source MATLAB software that int...
Brain tumours (BTs) are severe neurological disorders. They affect more than 308,000 people each year worldwide. The mortality rate is over 251,000 deaths annually (IARC, 2020 reports). Detecting BTs is complex because they vary in nature. Early diag...
Deep-neural-network-based artificial intelligence enables quantitative gait analysis with commodity sensors. However, current gait-analysis models are usually specialized for specific clinical populations and sensor settings due to the limited size a...
Early lung cancer invasive detection is important for further treatment and saving lives. In clinical practice, lung tumor invasiveness (LTI) detection is very challenging, imaging-based automatic prediction algorithms offer a non-invasive approach. ...
The ever-growing digital repositories of medical data provide opportunities for advanced healthcare by forming a foundation for a digital healthcare ecosystem. Such an ecosystem facilitates digitized solutions to aspects like early diagnosis, evidenc...
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