A Novel Handcrafted Features and Deep BiLSTM Neural Network for Lymphoma Recognition

The human lymphatic system is commonly affected by two primary forms of lymphoma disease: Hodgkin lymphoma and non-Hodgkin lymphoma. The second type, in particular, has emerged as a leading cause of patient mortality. Therefore, achieving a correct and early diagnosis is crucial for healthcare pract...

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Bibliographic Details
Main Authors: Ferjaoui, Radhia, Boujnah, Sana, Khalifa, Anouar Ben
Format: Conference Proceeding
Language:English
Subjects:
Accuracy
deep BiL-STM
Diseases
Feature extraction
Handcrafted
Histogram analysis
LSTM
Lymphatic system
Lymphoma Recognition
Medical services
morphological
Multimodality
Neural networks
Planning
Robustness
Shape
Solid modeling
Texture
VGG16
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Summary:The human lymphatic system is commonly affected by two primary forms of lymphoma disease: Hodgkin lymphoma and non-Hodgkin lymphoma. The second type, in particular, has emerged as a leading cause of patient mortality. Therefore, achieving a correct and early diagnosis is crucial for healthcare practitioners to devise suitable therapeutic strategies. For these reasons, in this work, We suggest the development of a computer-aided diagnosis system utilizing a novel hybrid approach that incorporates Handcrafted features and BiLSTM networks for the discrimination and analysis of patients with evolving lymphoma from those with residual masses who do not need re-treatment. Our proposed approach combines the concatenation of all extracted features obtained through various handcrafted methods (including histogram analysis, textural, and shape analysis) to analyze the functional, morphological, and anatomical aspects of each lesion. The "LWBDWMRI" databases were utilised for the experiment. We compared the experimental results of the suggested approach to each model: BiLSTM, LSTM, and VGG16. This comparison was conducted across five different approach cases: concatenating functional features only, textural features only, morphological features only, combining textural and morphological features to obtain global anatomical features, and incorporating both functional and anatomical criteria. The proposed approach achieved 96%, 97%, 98%, and 26.11 seconds for Accuracy, F1-score, Recall, and execution time, respectively.
ISSN:2576-3555
DOI:10.1109/CoDIT62066.2024.10708513