Tawsifur Rahman, Muhammad E. H. Chowdhury, Amith Khandakar, Khandaker Reajul Islam et al.
Pneumonia is a life-threatening disease, which occurs in the lungs caused by either bacterial or viral infection. It can be life-endangering if not acted upon at the right time and thus the early diagnosis of pneumonia is vital. The paper aims to automatically detect bacterial and viral pneumonia us...
Samiul Based Shuvo, Shams Nafisa Ali, Soham Irtiza Swapnil, Mabrook Al‐Rakhami et al.
The alarmingly high mortality rate and increasing global prevalence of cardiovascular diseases (CVDs) signify the crucial need for early detection schemes. Phonocardiogram (PCG) signals have been historically applied in this domain owing to its simplicity and cost-effectiveness. In this article, we ...
Shamima Akter, F. M. Javed Mehedi Shamrat, Sovon Chakraborty, Asif Karim et al.
COVID-19, regarded as the deadliest virus of the 21st century, has claimed the lives of millions of people around the globe in less than two years. Since the virus initially affects the lungs of patients, X-ray imaging of the chest is helpful for effective diagnosis. Any method for automatic, reliab...
Erick Andres Perez Alday, Annie Gu, Amit Shah, Chad Robichaux et al.
Abstract The subject of the PhysioNet/Computing in Cardiology Challenge 2020 was the identification of cardiac abnormalities in 12-lead electrocardiogram (ECG) recordings. A total of 66,405 recordings were sourced from hospital systems from four distinct countries and annotated with clinical diagnos...
Nihad Karim Chowdhury, Muhammad Ashad Kabir, Md. Muhtadir Rahman, Sheikh Mohammed Shariful Islam
This research aims to analyze the performance of state-of-the-art machine learning techniques for classifying COVID-19 from cough sounds and to identify the model(s) that consistently perform well across different cough datasets. Different performance evaluation metrics (precision, sensitivity, spec...
F. M. Javed Mehedi Shamrat, Sami Azam, Asif Karim, Md. Rakibul Islam et al.
In recent years, lung disease has increased manyfold, causing millions of casualties annually. To combat the crisis, an efficient, reliable, and affordable lung disease diagnosis technique has become indispensable. In this study, a multiclass classification of lung disease from frontal chest X-ray i...
Md. Kawsher Mahbub, Milon Biswas, Loveleen Gaur, Fayadh Alenezi et al.
Chest X-ray (CXR) imaging is a low-cost, easy-to-use imaging alternative that can be used to diagnose/screen pulmonary abnormalities due to infectious diseaseX: Covid-19, Pneumonia and Tuberculosis (TB). Not limited to binary decisions (with respect to healthy cases) that are reported in the state-o...
Qin Qin, Jianqing Li, Li Zhang, Yinggao Yue et al.
Automatic feature extraction and classification are two main tasks in abnormal ECG beat recognition. Feature extraction is an important prerequisite prior to classification since it provides the classifier with input features, and the performance of classifier depends significantly on the quality of...
Ahmed Imtiaz Humayun, Shabnam Ghaffarzadegan, Md. Istiaq Ansari, Zhe Feng et al.
OBJECTIVE: Cardiac auscultation is the most practiced non-invasive and cost-effective procedure for the early diagnosis of heart diseases. While machine learning based systems can aid in automatically screening patients, the robustness of these systems is affected by numerous factors including the s...
Md Shofiqul Islam, Khondokar Fida Hasan, Sunjida Sultana, Shahadat Uddin et al.
Deep learning-based models have achieved significant success in detecting cardiac arrhythmia by analyzing ECG signals to categorize patient heartbeats. To improve the performance of such models, we have developed a novel hybrid hierarchical attention-based bidirectional recurrent neural network with...
Md. Maruf Hossain, Md Shahin Ali, Md Mahfuz Ahmed, Md. Rakibul Hasan Rakib et al.
Cardiovascular disease (CVD) is a leading cause of death worldwide, with millions dying each year. The identification and early diagnosis of CVD are critical in preventing adverse health outcomes. Hence, this study proposes a hybrid deep learning (DL) model that combines a convolutional neural netwo...
Md Manjurul Ahsan, Md Tanvir Ahad, Farzana Akter Soma, Shuva Paul et al.
Chest radiographs (X-rays) combined with Deep Convolutional Neural Network (CNN) methods have been demonstrated to detect and diagnose the onset of COVID-19, the disease caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). However, questions remain regarding the accuracy of th...
Nahida Habib, Md. Mahmodul Hasan, Md. Mahfuz Reza, Mohammad Motiur Rahman
Pneumonia, an acute respiratory infection, causes serious breathing hindrance by damaging lung/s. Recovery of pneumonia patients depends on the early diagnosis of the disease and proper treatment. This paper proposes an ensemble method-based pneumonia diagnosis from Chest X-ray images. The deep Conv...
Md. Rashed-Al-Mahfuz, Mohammad Ali Moni, Píetro Lió, Sheikh Mohammed Shariful Islam et al.
Medical practitioners need to understand the critical features of ECG beats to diagnose and identify cardiovascular conditions accurately. This would be greatly facilitated by identifying the significant features of frequency components in temporal ECG wave-forms using computational methods. In this...
Senjuti Kabir, S. M. Mazidur Rahman, Shakil Ahmed, Md Shamiul Islam et al.
BACKGROUND: The World Health Organization recommends the Xpert MTB/RIF Ultra assay for diagnosing pulmonary tuberculosis (PTB) in children. Though stool is a potential alternative to respiratory specimens among children, the diagnostic performance of Xpert Ultra on stool is unknown. Thus, we assesse...
Md. Nahiduzzaman, Md. Omaer Faruq Goni, Rakibul Hassan, Md. Robiul Islam et al.
Numerous epidemic lung diseases such as COVID-19, tuberculosis (TB), and pneumonia have spread over the world, killing millions of people. Medical specialists have experienced challenges in correctly identifying these diseases due to their subtle differences in Chest X-ray images (CXR). To assist th...
Eram Mahamud, Nafiz Fahad, Md Assaduzzaman, S.M. Zain et al.
Traditional deep learning models are often considered “black boxes” due to their lack of interpretability, which limits their therapeutic use despite their success in classification tasks. This study aims to improve the interpretability of diagnoses for COVID-19, pneumonia, and tuberculosis from X-r...
Monisha Dey, Nuzaer Omar, Muhammad Ahsan Ullah
Heart attack else wise termed as myocardial infarction (MI) causes irreparable death of cardiac muscles yielding the focal reason for most casualties among all cardiovascular diseases (CVDs’). A 12-lead electrocardiogram (ECG) generally depicts cardiac abnormalities and so customary deep learning (D...
Tanjim Mahmud, Anik Barua, Dilshad Islam, Mohammad Shahadat Hossain et al.
The classification and identification of arrhythmias using ECG signals hold substantial practical importance in the early prevention and detection of cardiac/cardiovascular disorders. Traditional ECG interpretation, relying on human clinical judgment, is susceptible to errors due to fatigue. Our met...
Shams Nafisa Ali, Samiul Based Shuvo, Muhammad Ishtiaque Sayeed Al-Manzo, Anwarul Hasan et al.
Objective: The heart sound signals captured via a digital stethoscope are often distorted by environmental and physiological noise, altering their salient and critical properties. The problem is exacerbated in crowded low-resource hospital settings with high noise levels which degrades the diagnosti...