Anatomy-XNet: An Anatomy Aware Convolutional Neural Network for Thoracic Disease Classification in Chest X-Rays

Kamal, Uday; Mohammad, Zunaed,; Nizam, Nusrat Binta; Hasan, Taufiq

doi:10.1109/jbhi.2022.3199594

Cited by 19 publications

(17 citation statements)

References 36 publications

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“…The suggested model employs low-complexity feature extraction methods and BFO with multiple CNN approaches, enabling extremely accurate assessment and correlation across a broad spectrum of illness kinds. The proposed model enhanced illness classification and recommendation accuracy by 12.4%, 1.5%, and 5.9% under various use situations compared to ACNN (Kamal et al 2022 ), MGAN (Liu et al 2022 ), and DLV3 (Zhou et al 2022 ), as evaluated using various test samples in Table 2 and Fig. 5 .…”

Section: Comparative Results Analysismentioning

confidence: 97%

“…Combining several CNN techniques with multidomain feature representation models enables the suggested model to deliver accurate recommendations. The proposed model improved disease classification and associated recommendation precision by 5.9% when compared to ACNN (Kamal et al 2022 ), 0.5% when compared to MGAN (Liu et al 2022 ), and 6.0% when compared to DLV3 (Zhou et al 2022 ) in a variety of application scenarios, based on the estimated accuracy for various test samples in Table 3 and Fig. 6 .…”

Section: Comparative Results Analysismentioning

confidence: 99%

“…Eighty percent of these samples were put to use in the training of the CNN and MAHP models, while the remaining twenty percent were put to use in testing and validation procedures. Based on this segregation, the accuracy of recommendation was compared with ACNN (Kamal et al 2022 ), MGAN (Liu et al 2022 ), and DLV3 (Zhou et al 2022 ) in Table 1 w.r.t. number of evaluation samples (NES) for different scenarios.…”

Section: Methodsmentioning

confidence: 99%

“…Living with cancer can be a long-term, arduous experience that can negatively impact a person’s mental health. The side effects of cancer treatments, such as chemotherapy and radiation therapy, can negatively impact a patient’s mental and physical health via Anatomy Aware Convolutional Neural Network (ACNNs) and their extensions (Kamal et al 2022 ; Biswas et al 2021 ; Rehman 2021 ; https://data.4tu.nl/articles/dataset/Geothermal_Project_on_TU_Delft_Campus_-_DAPGEO-02_Core_CT-Scan_Data/21528819 ). Mental health conditions such as depression and anxiety can increase the risk of developing certain types of cancer or worsen the prognosis for cancer patients.…”

Section: Introductionmentioning

confidence: 99%

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RETRACTED ARTICLE: DLMBHCO: design of an augmented bioinspired deep learning-based multidomain body parameter analysis via heterogeneous correlative body organ analysis

Ajani¹,

Mulla²,

Limkar³

et al. 2023

Soft Comput

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Progressive organ-level disorders in the human body are often correlated with diseases in other body parts. For instance, liver diseases can be linked with heart issues, while cancers can be linked with brain diseases (or psychological conditions). Defining such correlations is a complex task, and existing deep learning models that perform this task either showcase lower accuracy or are non-comprehensive when applied to real-time scenarios. To overcome these issues, this text proposes design of an augmented bioinspired deep learning-based multidomain body parameter analysis via heterogeneous correlative body organ analysis. The proposed model initially collects temporal and spatial data scans for different body parts and uses a multidomain feature extraction engine to convert these scans into vector sets. These vectors are processed by a Bacterial Foraging Optimizer (BFO), which assists in identification of highly variant feature sets, which are individually classified into different disease categories. A fusion of Inception Net, XCeption Net, and GoogLeNet Models is used to perform these classifications. The classified categories are linked with other disease types via temporal analysis of blood reports. The temporal analysis engine uses Modified Analytical Hierarchical Processing (MAHP) Model for calculating inter-organ disease dependency probabilities. Based on these probabilities, the model is able to generate a patient-level correlation map, which can be used by clinical experts to suggest remedial treatments, due to which the model was able to identify correlations between brain disorders and kidneys, heart diseases and lungs, heart diseases and liver, brain diseases and different types of cancers with high efficiency when evaluated under clinical scenarios. When validated on MITBIH, DEAP, CT Kidney, RIDER, and PLCO data samples, it was observed that the proposed model was capable of improving accuracy of correlation by 8.5%, while improving precision and recall by 3.2% when compared with existing correlation models under similar clinical scenarios.

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Section: Comparative Results Analysismentioning

confidence: 97%

Section: Comparative Results Analysismentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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RETRACTED ARTICLE: DLMBHCO: design of an augmented bioinspired deep learning-based multidomain body parameter analysis via heterogeneous correlative body organ analysis

Ajani¹,

Mulla²,

Limkar³

et al. 2023

Soft Comput

View full text Add to dashboard Cite

show abstract

“…However, these methods are data-driven and are generally agnostic to the human anatomy and its dependence on identifying the diseased regions. Chen et al [7] and Kamal et al [8] utilized lung segmentation-based attention mechanisms. However, diseasespecific anatomical prior knowledge was not considered within the attention mechanism and abnormality localization.…”

Section: Introductionmentioning

confidence: 99%

A Novel Attention Mechanism Using Anatomical Prior Probability Maps for Thoracic Disease Classification from X-Ray Images

Hossain¹,

Hossain²,

Mohammad³

et al. 2022

Preprint

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Computer-aided disease diagnosis and prognosis based on medical images is a rapidly emerging field. Many Convolutional Neural Network (CNN) architectures have been developed by researchers for disease classification and localization from chest X-ray images. It is known that different thoracic disease lesions are more likely to occur in specific anatomical regions compared to others. Based on this knowledge, we first estimate a diseasedependent spatial probability, i.e., an anatomical prior, that indicates the probability of occurrence of a disease in a specific region in a chest X-ray image. Next, we develop a novel attention-based classification model that combines information from the estimated anatomical prior and automatically extracted chest region of interest (ROI) masks to provide attention to the feature maps generated from a deep convolution network. Unlike previous works that utilize various self-attention mechanisms, the proposed method leverages the extracted chest ROI masks along with the probabilistic anatomical prior information, which selects the region of interest for different diseases to provide attention. The proposed method shows superior performance in disease classification on the NIH ChestX-ray14 dataset compared to existing state-of-the-art methods while reaching an area under the ROC curve (AUC) of 0.8427. Regarding disease localization, the proposed method shows competitive performance compared to state-of-the-art methods, achieving an accuracy of 61% with an Intersection over Union (IoU) threshold of 0.3. The proposed method can also be generalized to other medical image-based disease classification and localization tasks where the probability of occurrence of the lesion is dependent on specific anatomical sites.

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DEAN: Improving the Efficiency of Disease Network Using Selective Ensemble Network Aggregation Model

Ahuja,

Shrimankar,

Durge

2024

Lecture Notes in Networks and Systems

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Anatomy-XNet: An Anatomy Aware Convolutional Neural Network for Thoracic Disease Classification in Chest X-Rays

Cited by 19 publications

References 36 publications

RETRACTED ARTICLE: DLMBHCO: design of an augmented bioinspired deep learning-based multidomain body parameter analysis via heterogeneous correlative body organ analysis

RETRACTED ARTICLE: DLMBHCO: design of an augmented bioinspired deep learning-based multidomain body parameter analysis via heterogeneous correlative body organ analysis

A Novel Attention Mechanism Using Anatomical Prior Probability Maps for Thoracic Disease Classification from X-Ray Images

DEAN: Improving the Efficiency of Disease Network Using Selective Ensemble Network Aggregation Model

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