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16+ results
Field: Digital Imaging for Blood Diseases

A combined deep CNN-LSTM network for the detection of novel coronavirus (COVID-19) using X-ray images

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Md. Zabirul Islam, Md. Milon Islam, Amanullah Asraf

Journal: Informatics in Medicine Unlocked
Year: 2020
Citations: 623

Nowadays, automatic disease detection has become a crucial issue in medical science due to rapid population growth. An automatic disease detection framework assists doctors in the diagnosis of disease and provides exact, consistent, and fast results and reduces the death rate. Coronavirus (COVID-19)...

Health SciencesMedicineRadiology, Nuclear Medicine and ImagingOpen Access
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Pre-trained convolutional neural networks as feature extractors toward improved malaria parasite detection in thin blood smear images

Verified

Sivaramakrishnan Rajaraman, Sameer Antani, Mahdieh Poostchi, Kamolrat Silamut et al.

Journal: PeerJYear: 2018Citations: 549

parasites transmitted through the bite of female Anopheles mosquito. Microscopists commonly examine thick and thin blood smears to diagnose disease and compute parasitemia. However, their accuracy depends on smear quality and expertise in classifying and counting parasitized and uninfected cells. Su...

Physical SciencesComputer ScienceComputer Vision and Pattern RecognitionOpen Access
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CNN-based image analysis for malaria diagnosis

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Zhaohui Liang, Andrew J. Powell, Ilker Ersoy, Mahdieh Poostchi et al.

Year: 2016Citations: 321

Malaria is a major global health threat. The standard way of diagnosing malaria is by visually examining blood smears for parasite-infected red blood cells under the microscope by qualified technicians. This method is inefficient and the diagnosis depends on the experience and the knowledge of the p...

Physical SciencesComputer ScienceComputer Vision and Pattern Recognition
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Robust clinical applicable CNN and U-Net based algorithm for MRI classification and segmentation for brain tumor

Verified

Atika Akter, Nazeela Nosheen, Sabbir Ahmed, Mariom Hossain et al.

Journal: Expert Systems with ApplicationsYear: 2023Citations: 242

Early diagnosis of brain tumors is critical for enhancing patient prognosis and treatment options, while accurate classification and segmentation of brain tumors are vital for developing personalized treatment strategies. Despite the widespread use of Magnetic Resonance Imaging (MRI) for brain exami...

Life SciencesNeuroscienceNeurologyOpen Access
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Machine learning approach of automatic identification and counting of blood cells

Verified

Mohammad Mahmudul Alam, Mohammad Tariqul Islam

Journal: Healthcare Technology LettersYear: 2019Citations: 202

A complete blood cell count is an important test in medical diagnosis to evaluate overall health condition. Traditionally blood cells are counted manually using haemocytometer along with other laboratory equipment's and chemical compounds, which is a time-consuming and tedious task. In this work, th...

Physical SciencesComputer ScienceComputer Vision and Pattern RecognitionOpen Access
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An efficient deep learning model to categorize brain tumor using reconstruction and fine-tuning

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Md. Alamin Talukder, Md. Manowarul Islam, Md. Ashraf Uddin, Arnisha Akhter et al.

Journal: Expert Systems with ApplicationsYear: 2023Citations: 193

Brain tumors are among the most fatal and devastating diseases, often resulting in significantly reduced life expectancy. An accurate diagnosis of brain tumors is crucial to devise treatment plans that can extend the lives of affected individuals. Manually identifying and analyzing large volumes of ...

Life SciencesNeuroscienceNeurologyOpen Access
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Deep Learning Based Automatic Malaria Parasite Detection from Blood Smear and Its Smartphone Based Application

Verified

K. M. Faizullah Fuhad, Jannat Ferdousey Tuba, Md. Rabiul Ali Sarker, Sifat Momen et al.

Journal: DiagnosticsYear: 2020Citations: 180

Malaria is a life-threatening disease that is spread by the Plasmodium parasites. It is detected by trained microscopists who analyze microscopic blood smear images. Modern deep learning techniques may be used to do this analysis automatically. The need for the trained personnel can be greatly reduc...

Physical SciencesComputer ScienceComputer Vision and Pattern RecognitionOpen Access
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EMCNet: Automated COVID-19 diagnosis from X-ray images using convolutional neural network and ensemble of machine learning classifiers

Verified

Prottoy Saha, Muhammad Sheikh Sadi, Md. Milon Islam

Journal: Informatics in Medicine UnlockedYear: 2020Citations: 161

Recently, coronavirus disease (COVID-19) has caused a serious effect on the healthcare system and the overall global economy. Doctors, researchers, and experts are focusing on alternative ways for the rapid detection of COVID-19, such as the development of automatic COVID-19 detection systems. In th...

Health SciencesMedicineRadiology, Nuclear Medicine and ImagingOpen Access
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COVID faster R–CNN: A novel framework to Diagnose Novel Coronavirus Disease (COVID-19) in X-Ray images

Verified

Kabid Hassan Shibly, Samrat Kumar Dey, Md Tahzib-Ul Islam, Md Mahbubur Rahman

Journal: Informatics in Medicine UnlockedYear: 2020Citations: 159

COVID-19 or novel coronavirus disease, which has already been declared as a worldwide pandemic, at first had an outbreak in a large city of China, named Wuhan. More than two hundred countries around the world have already been affected by this severe virus as it spreads by human interaction. Moreove...

Health SciencesMedicineRadiology, Nuclear Medicine and ImagingOpen Access
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Automatic recognition of severity level for diagnosis of diabetic retinopathy using deep visual features

Verified

Qaisar Abbas, Irene Fondón, Auxiliadora Sarmiento, Soledad Jiménez et al.

Journal: Medical & Biological Engineering & ComputingYear: 2017Citations: 152

Diabetic retinopathy (DR) is leading cause of blindness among diabetic patients. Recognition of severity level is required by ophthalmologists to early detect and diagnose the DR. However, it is a challenging task for both medical experts and computer-aided diagnosis systems due to requiring extensi...

Health SciencesMedicineRadiology, Nuclear Medicine and Imaging
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A Hybrid Dependable Deep Feature Extraction and Ensemble-Based Machine Learning Approach for Breast Cancer Detection

Verified

Selina Sharmin, Tanvir Ahammad, Md. Alamin Talukder, Partho Ghose

Journal: IEEE AccessYear: 2023Citations: 146

Breast cancer is a prevalent and life-threatening disease that requires effective detection and diagnosis methods to improve patient outcomes. Deep learning (DL) and machine learning (ML) techniques have emerged as powerful tools in breast cancer detection, offering benefits such as improved accurac...

Physical SciencesComputer ScienceArtificial IntelligenceOpen Access
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Severity Classification of Diabetic Retinopathy Using an Ensemble Learning Algorithm through Analyzing Retinal Images

Verified

Niloy Sikder, Mehedi Masud, Anupam Kumar Bairagi, Abu Shamim Mohammad Arif et al.

Journal: SymmetryYear: 2021Citations: 143

Diabetic Retinopathy (DR) refers to the damages endured by the retina as an effect of diabetes. DR has become a severe health concern worldwide, as the number of diabetes patients is soaring uncountably. Periodic eye examination allows doctors to detect DR in patients at an early stage to initiate p...

Health SciencesMedicineRadiology, Nuclear Medicine and ImagingOpen Access
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A Belief Rule Based Expert System to Assess Tuberculosis under Uncertainty

Verified

Mohammad Shahadat Hossain, Faisal Ahmed, Fatema-Tuj-Johora, Karl Andersson

Journal: Journal of Medical SystemsYear: 2017Citations: 129

The primary diagnosis of Tuberculosis (TB) is usually carried out by looking at the various signs and symptoms of a patient. However, these signs and symptoms cannot be measured with 100 % certainty since they are associated with various types of uncertainties such as vagueness, imprecision, randomn...

Health SciencesHealth ProfessionsHealth Information ManagementOpen Access
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Diabetic retinopathy identification using parallel convolutional neural network based feature extractor and ELM classifier

Verified

Md. Nahiduzzaman, Md. Robiul Islam, Md. Omaer Faruq Goni, Md. Shamim Anower et al.

Journal: Expert Systems with ApplicationsYear: 2023Citations: 128

Diabetic retinopathy (DR) is an incurable retinal condition caused by excessive blood sugar that, if left untreated, can result in even blindness. A novel automated technique for DR detection has been proposed in this paper. To accentuate the lesions, the fundus images (FIs) were preprocessed using ...

Health SciencesMedicineRadiology, Nuclear Medicine and ImagingOpen Access
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Complex features extraction with deep learning model for the detection of COVID19 from CT scan images using ensemble based machine learning approach

Verified

Md. Robiul Islam, Md. Nahiduzzaman

Journal: Expert Systems with ApplicationsYear: 2022Citations: 124

Recently the most infectious disease is the novel Coronavirus disease (COVID 19) creates a devastating effect on public health in more than 200 countries in the world. Since the detection of COVID19 using reverse transcription-polymerase chain reaction (RT-PCR) is time-consuming and error-prone, the...

Health SciencesMedicineRadiology, Nuclear Medicine and ImagingOpen Access
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