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Qingji Guan, Qinrun Chen and Yaping Huang
Chest X-ray image classification suffers from the high inter-similarity in appearance that is vulnerable to noisy labels. The data-dependent and heteroscedastic characteristic label noise make chest X-ray image classification more challenging. To address...
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Shaw-Hwa Lo and Yiqiao Yin
The field of explainable artificial intelligence (XAI) aims to build explainable and interpretable machine learning (or deep learning) methods without sacrificing prediction performance. Convolutional neural networks (CNNs) have been successful in making...
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G. Raghavendra Prasad
In medical imaging, the scope of image enhancement is highly challenging. Here digital chest x ?ray image are taken in a spatial domain and enhancement of the image is done through histogram equalization method. Histogram equalization is a specific case ...
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Shoffan Saifullah and Rafal Drezewski
Accurate medical image segmentation is paramount for precise diagnosis and treatment in modern healthcare. This research presents a comprehensive study of the efficacy of particle swarm optimization (PSO) combined with histogram equalization (HE) preproc...
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Domantas Kuzinkovas and Sandhya Clement
Advances in the field of image classification using convolutional neural networks (CNNs) have greatly improved the accuracy of medical image diagnosis by radiologists. Numerous research groups have applied CNN methods to diagnose respiratory illnesses fr...
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Awf A. Ramadhan and Muhammet Baykara
The novel coronavirus (COVID-19) is a contagious viral disease that has rapidly spread worldwide since December 2019, causing the disruption of life and heavy economic losses. Since the beginning of the virus outbreak, a polymerase chain reaction has bee...
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Ku Muhammad Naim Ku Khalif, Woo Chaw Seng, Alexander Gegov, Ahmad Syafadhli Abu Bakar and Nur Adibah Shahrul
Convolutional Neural Networks (CNNs) have garnered significant utilisation within automated image classification systems. CNNs possess the ability to leverage the spatial and temporal correlations inherent in a dataset. This study delves into the use of ...
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Biprodip Pal, Debashis Gupta, Md. Rashed-Al-Mahfuz, Salem A. Alyami and Mohammad Ali Moni
The COVID-19 pandemic requires the rapid isolation of infected patients. Thus, high-sensitivity radiology images could be a key technique to diagnose patients besides the polymerase chain reaction approach. Deep learning algorithms are proposed in severa...
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Tarik El Lel, Mominul Ahsan and Julfikar Haider
Starting in late 2019, the coronavirus SARS-CoV-2 began spreading around the world and causing disruption in both daily life and healthcare systems. The disease is estimated to have caused more than 6 million deaths worldwide [WHO]. The pandemic and the ...
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David Clement, Emmanuel Agu, John Obayemi, Steve Adeshina and Wole Soboyejo
Breast cancer accounts for 30% of all female cancers. Accurately distinguishing dangerous malignant tumors from benign harmless ones is key to ensuring patients receive lifesaving treatments on time. However, as doctors currently do not identify 10% to 3...
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Sagar Kora Venu and Sridhar Ravula
Medical image datasets are usually imbalanced due to the high costs of obtaining the data and time-consuming annotations. Training a deep neural network model on such datasets to accurately classify the medical condition does not yield the desired result...
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Khandaker Foysal Haque and Ahmed Abdelgawad
Deep Learning has improved multi-fold in recent years and it has been playing a great role in image classification which also includes medical imaging. Convolutional Neural Networks (CNNs) have been performing well in detecting many diseases including co...
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