Jafar Abbas

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Verified

Jafar verified their affiliation via an institutional email.

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• Doctor of Engineering
• PostDoc Researcher at Myongji University

Kidney disease is a global health concern, impacting a substantial part of the overall population and contributing to high morbidity and mortality rates. The initially diagnosed phases of kidney disease are often present without noticeable indications, leading to delayed diagnosis and treatment. Therefore, early detection is crucial to reducing com...

Colorectal cancer (CRC) remains a significant health concern, with colonoscopy serving as the gold standard for diagnosis. Accurately segmenting polyps from colonoscopy images is crucial for detecting polyps and preventing CRC. However, challenges such as varying polyp sizes, blurred edges, and uneven brightness hinder segmentation accuracy. Levera...

Accurately segmenting and staging tumor lesions in cancer patients presents a significant challenge for radiologists, but it is essential for devising effective treatment plans including radiation therapy, personalized medicine, and surgical options. The integration of artificial intelligence (AI), particularly deep learning (DL), has become a usef...

Accurate and rapid plant disease detection is critical for enhancing long-term agricultural yield. Disease infection poses the most significant challenge in crop production, potentially leading to economic losses. Viruses, fungi, bacteria, and other infectious organisms can affect numerous plant parts, including roots, stems, and leaves. Traditiona...

Recently, the world has been dealing with a severe outbreak of COVID-19. The rapid transmission of the virus causes mild to severe cases of cough, fever, body aches, organ failures, and death. An increasing number of patients, fewer diagnostic options, and extended waiting periods for test results all put pressure on healthcare systems, increasing...

Machine learning (ML) has proven to be highly effective in solving complex problems in various domains, thanks to its ability to identify specific data tasks, perform feature engineering, and learn quickly. However, designing and training ML models is a complicated task and requires optimization. The effectiveness of ML models is highly dependent o...

COVID-19 is a viral pandemic disease that spreads widely all around the world. The only way to identify COVID-19 patients at an early stage is to stop the spread of the virus. Different approaches are used to diagnose, such as RT-PCR, Chest X-rays, and CT images. However, these are time-consuming and require a specialized lab. Therefore, there is a...

Early prediction of cardiovascular disease is crucial for medical experts to make informed decisions. Effective diagnosis of heart disease can help prevent heart failure, heart attacks, stroke, and coronary artery disease. This paper aims to build a high-accuracy heart disease prediction system using machine learning. For this purpose, an automatic...

Semantic segmentation for diagnosing chest-related diseases like cardiomegaly, emphysema, pleural effusions, and pneumothorax is a critical yet understudied tool for identifying the chest anatomy. A dangerous disease among these is cardiomegaly, in which sudden death is a high risk. An expert medical practitioner can diagnose cardiomegaly early usi...

Convolutional Neural Network (CNN) is one of the most widely used deep learning models in pattern and image recognition. It can train a large number of datasets and get valuable results. The deep Residual Network (ResNet) is one of the most innovative CNN architecture to train thousands of layers or more and leads to high performance for complex pr...

Convolutional Neural Networks (CNNs) is one of the most commonly used deep learning models to train a large number of datasets and getting valuable results in image recognition. Deep Residual Learning (ResNet) is one of the most famous CNN for the computer vision tasks that won the ILSVR-2015 classification competition. ResNet is also one of the de...

Convolutional Neural Network (CNN) is one of the most commonly used deep learning models to train a large number of datasets and getting valuable results in image recognition. Deep Residual Learning (ResNet) is one of the most famous CNNs for the computer vision tasks that won the ILSVR-2015 classification competition. ResNet is also one of the dee...