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  1. Article ; Online: ACDSSNet: Atrous Convolution-based Deep Semantic Segmentation Network for Efficient Detection of Sickle Cell Anemia.

    Das, Pradeep Kumar / Dash, Abinash / Meher, Sukadev

    IEEE journal of biomedical and health informatics

    2024  Volume PP

    Abstract: In medical image processing, semantic segmentation plays an important role since, in most applications, it is required to find the exact location of the anomaly. It is tough than the segmentation or classification task since in this task class- ... ...

    Abstract In medical image processing, semantic segmentation plays an important role since, in most applications, it is required to find the exact location of the anomaly. It is tough than the segmentation or classification task since in this task class-belongingness of each pixel is predicted. The presence of noise, and variations of viewpoint, shape, and size of cells make it more challenging. In this work, two novel Atrous Convolution-based Deep Semantic Segmentation Networks: ACDSSNet-I, ACDSSNet-II are proposed for more accurate Sickle Cell Anemia (SCA) detection, which can mitigate these issues. The main contributions are: 1) Improvement of feature extraction performance by employing Atrous convolution-based dense prediction, which yields varying field-view with adaptive resolution; 2) Employment of Atrous spatial pyramid-based pooling resulting in more robust segmentation; 3) Upgrading the segmentation performance by adding an efficient decoder module to finetune the segmentation, particularly at object boundaries; 4) Design of modified DeepLabV3+ architectures (MDA) by introducing computationally efficient MobileNetV2 or ResNet50 as a base classifier; 5) Further performance improvement has been accomplished by hybridizing MDA-1 with MDA-2 by integrating the benefits of MobileNetV2 models and ADAM and SGDM optimizers; 6) Improvement of overall performance by efficiently utilizing the input image's saturation information only to minimize the false positive. Furthermore, the optimal selection of threshold value makes the hybridization of MDA-1 with MDA-2 efficient resulting in more accurate semantic segmentation. The experimental results illustrate the proposed model outperforms others with the best semantic segmentation performances: 98.21% accuracy, 99.00% specificity, and 0.9547 DSC value.
    Language English
    Publishing date 2024-02-06
    Publishing country United States
    Document type Journal Article
    ZDB-ID 2695320-1
    ISSN 2168-2208 ; 2168-2194
    ISSN (online) 2168-2208
    ISSN 2168-2194
    DOI 10.1109/JBHI.2024.3362843
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: An Efficient Detection and Classification of Acute Leukemia Using Transfer Learning and Orthogonal Softmax Layer-Based Model.

    Das, Pradeep Kumar / Sahoo, Biswajeet / Meher, Sukadev

    IEEE/ACM transactions on computational biology and bioinformatics

    2023  Volume 20, Issue 3, Page(s) 1817–1828

    Abstract: For the early diagnosis of hematological disorders like blood cancer, microscopic analysis of blood cells is very important. Traditional deep CNNs lead to overfitting when it receives small medical image datasets such as ALLIDB1, ALLIDB2, and ASH. This ... ...

    Abstract For the early diagnosis of hematological disorders like blood cancer, microscopic analysis of blood cells is very important. Traditional deep CNNs lead to overfitting when it receives small medical image datasets such as ALLIDB1, ALLIDB2, and ASH. This paper proposes a new and effective model for classifying and detecting Acute Lymphoblastic Leukemia (ALL) or Acute Myelogenous Leukemia (AML) that delivers excellent performance in small medical datasets. Here, we have proposed a novel Orthogonal SoftMax Layer (OSL)-based Acute Leukemia detection model that consists of ResNet 18-based deep feature extraction followed by efficient OSL-based classification. Here, OSL is integrated with the ResNet18 to improve the classification performance by making the weight vectors orthogonal to each other. Hence, it integrates ResNet benefits (residual learning and identity mapping) with the benefits of OSL-based classification (improvement of feature discrimination capability and computational efficiency). Furthermore, we have introduced extra dropout and ReLu layers in the architecture to achieve a faster network with enhanced performance. The performance verification is performed on standard ALLIDB1, ALLIDB2, and C_NMC_2019 datasets for efficient ALL detection and ASH dataset for effective AML detection. The experimental performance demonstrates the superiority of the proposed model over other compairing models.
    MeSH term(s) Humans ; Leukemia, Myeloid, Acute/diagnosis ; Leukemia, Myeloid, Acute/genetics ; Machine Learning
    Language English
    Publishing date 2023-06-05
    Publishing country United States
    Document type Journal Article
    ISSN 1557-9964
    ISSN (online) 1557-9964
    DOI 10.1109/TCBB.2022.3218590
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article ; Online: Recent advancements in machine learning and deep learning-based breast cancer detection using mammograms.

    Sahu, Adyasha / Das, Pradeep Kumar / Meher, Sukadev

    Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)

    2023  Volume 114, Page(s) 103138

    Abstract: Objective: Mammogram-based automatic breast cancer detection has a primary role in accurate cancer diagnosis and treatment planning to save valuable lives. Mammography is one basic yet efficient test for screening breast cancer. Very few comprehensive ... ...

    Abstract Objective: Mammogram-based automatic breast cancer detection has a primary role in accurate cancer diagnosis and treatment planning to save valuable lives. Mammography is one basic yet efficient test for screening breast cancer. Very few comprehensive surveys have been presented to briefly analyze methods for detecting breast cancer with mammograms. In this article, our objective is to give an overview of recent advancements in machine learning (ML) and deep learning (DL)-based breast cancer detection systems.
    Methods: We give a structured framework to categorize mammogram-based breast cancer detection techniques. Several publicly available mammogram databases and different performance measures are also mentioned.
    Results: After deliberate investigation, we find most of the works classify breast tumors either as normal-abnormal or malignant-benign rather than classifying them into three classes. Furthermore, DL-based features are more significant than hand-crafted features. However, transfer learning is preferred over others as it yields better performance in small datasets, unlike classical DL techniques.
    Significance and conclusion: In this article, we have made an attempt to give recent advancements in artificial intelligence (AI)-based breast cancer detection systems. Furthermore, a number of challenging issues and possible research directions are mentioned, which will help researchers in further scopes of research in this field.
    MeSH term(s) Humans ; Female ; Breast Neoplasms/diagnostic imaging ; Breast Neoplasms/pathology ; Deep Learning ; Artificial Intelligence ; Mammography/methods ; Machine Learning
    Language English
    Publishing date 2023-09-28
    Publishing country Italy
    Document type Journal Article ; Review
    ZDB-ID 1122650-x
    ISSN 1724-191X ; 1120-1797
    ISSN (online) 1724-191X
    ISSN 1120-1797
    DOI 10.1016/j.ejmp.2023.103138
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.B 2941: Show issues Location:
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  4. Article ; Online: A Unified Local-Global Feature Extraction Network for Human Gait Recognition Using Smartphone Sensors.

    Das, Sonia / Meher, Sukadev / Sahoo, Upendra Kumar

    Sensors (Basel, Switzerland)

    2022  Volume 22, Issue 11

    Abstract: Smartphone-based gait recognition has been considered a unique and promising technique for biometric-based identification. It is integrated with multiple sensors to collect inertial data while a person walks. However, captured data may be affected by ... ...

    Abstract Smartphone-based gait recognition has been considered a unique and promising technique for biometric-based identification. It is integrated with multiple sensors to collect inertial data while a person walks. However, captured data may be affected by several covariate factors due to variations of gait sequences such as holding loads, wearing types, shoe types, etc. Recent gait recognition approaches either work on global or local features, causing failure to handle these covariate-based features. To address these issues, a novel weighted multi-scale CNN (WMsCNN) architecture is designed to extract local to global features for boosting recognition accuracy. Specifically, a weight update sub-network (Ws) is proposed to increase or reduce the weights of features concerning their contribution to the final classification task. Thus, the sensitivity of these features toward the covariate factors decreases using the weight updated technique. Later, these features are fed to a fusion module used to produce global features for the overall classification. Extensive experiments have been conducted on four different benchmark datasets, and the demonstrated results of the proposed model are superior to other state-of-the-art deep learning approaches.
    MeSH term(s) Benchmarking ; Biometric Identification/methods ; Gait ; Humans ; Recognition, Psychology ; Smartphone
    Language English
    Publishing date 2022-05-24
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2052857-7
    ISSN 1424-8220 ; 1424-8220
    ISSN (online) 1424-8220
    ISSN 1424-8220
    DOI 10.3390/s22113968
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  5. Article ; Online: An Efficient Blood-Cell Segmentation for the Detection of Hematological Disorders.

    Das, Pradeep Kumar / Meher, Sukadev / Panda, Rutuparna / Abraham, Ajith

    IEEE transactions on cybernetics

    2022  Volume 52, Issue 10, Page(s) 10615–10626

    Abstract: The automatic segmentation of blood cells for detecting hematological disorders is a crucial job. It has a vital role in diagnosis, treatment planning, and output evaluation. The existing methods suffer from the issues like noise, improper seed-point ... ...

    Abstract The automatic segmentation of blood cells for detecting hematological disorders is a crucial job. It has a vital role in diagnosis, treatment planning, and output evaluation. The existing methods suffer from the issues like noise, improper seed-point detection, and oversegmentation problems, which are solved here using a Laplacian-of-Gaussian (LoG)-based modified highboosting operation, bounded opening followed by fast radial symmetry (BOFRS)-based seed-point detection, and hybrid ellipse fitting (EF), respectively. This article proposes a novel hybrid EF-based blood-cell segmentation approach, which may be used for detecting various hematological disorders. Our prime contributions are: 1) more accurate seed-point detection based on BO-FRS; 2) a novel least-squares (LS)-based geometric EF approach; and 3) an improved segmentation performance by employing a hybridized version of geometric and algebraic EF techniques retaining the benefits of both approaches. It is a computationally efficient approach since it hybridizes noniterative-geometric and algebraic methods. Moreover, we propose to estimate the minor and major axes based on the residue and residue offset factors. The residue offset parameter, proposed here, yields more accurate segmentation with proper EF. Our method is compared with the state-of-the-art methods. It outperforms the existing EF techniques in terms of dice similarity, Jaccard score, precision, and F1 score. It may be useful for other medical and cybernetics applications.
    MeSH term(s) Algorithms ; Blood Cells ; Least-Squares Analysis
    Language English
    Publishing date 2022-09-19
    Publishing country United States
    Document type Journal Article
    ISSN 2168-2275
    ISSN (online) 2168-2275
    DOI 10.1109/TCYB.2021.3062152
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  6. Article ; Online: A Review of Automated Methods for the Detection of Sickle Cell Disease.

    Das, Pradeep Kumar / Meher, Sukadev / Panda, Rutuparna / Abraham, Ajith

    IEEE reviews in biomedical engineering

    2019  Volume 13, Page(s) 309–324

    Abstract: Detection of sickle cell disease is a crucial job in medical image analysis. It emphasizes elaborate analysis of proper disease diagnosis after accurate detection followed by a classification of irregularities, which plays a vital role in the sickle cell ...

    Abstract Detection of sickle cell disease is a crucial job in medical image analysis. It emphasizes elaborate analysis of proper disease diagnosis after accurate detection followed by a classification of irregularities, which plays a vital role in the sickle cell disease diagnosis, treatment planning, and treatment outcome evaluation. Proper segmentation of complex cell clusters makes sickle cell detection more accurate and robust. Cell morphology has a key role in the detection of the sickle cell because the shapes of the normal blood cell and sickle cell differ significantly. This review emphasizes state-of-the-art methods and recent advances in detection, segmentation, and classification of sickle cell disease. We discuss key challenges encountered during the segmentation of overlapping blood cells. Moreover, standard validation measures that have been employed to yield performance analysis of various methods are also discussed. The methodologies and experiments in this review will be useful to further research and work in this area.
    MeSH term(s) Algorithms ; Anemia, Sickle Cell/diagnostic imaging ; Anemia, Sickle Cell/pathology ; Databases, Factual ; Erythrocytes/cytology ; Erythrocytes/pathology ; Humans ; Image Interpretation, Computer-Assisted/methods ; Microscopy ; Neural Networks, Computer
    Language English
    Publishing date 2019-05-20
    Publishing country United States
    Document type Journal Article ; Review
    ISSN 1941-1189
    ISSN (online) 1941-1189
    DOI 10.1109/RBME.2019.2917780
    Database MEDical Literature Analysis and Retrieval System OnLINE

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