Class dependency-based learning using Bi-LSTM coupled with the transfer learning of VGG16 for the diagnosis of tuberculosis from chest X-rays

Chowdary, G. Jignesh; Suganya, G.; Premalatha, M.; Karunamurthy, K

doi:10.1049/pbhe040e_ch3

Cited by 3 publications

(1 citation statement)

References 39 publications

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“…Over the last few decades, there has been a lot of research that is done in automating several medical practices with AI via machine learning and deep learning 5 . These methods have shown promising results in the diagnosis of Pneumonia, Brain tumors, Heart diseases, COVID-19, Tuberculosis 6 , and also in the diagnosis of other cancers like breast 7 , lung, and brain. Even in this field, there are several studies that were proposed for the screening of cervical cancer from cervical cytology images 3 , 8 ‐ 10 .…”

Section: Introductionmentioning

confidence: 99%

Nucleus segmentation and classification using residual SE-UNet and feature concatenation approach incervical cytopathology cell images

Chowdary

Suganya

Premalatha

et al. 2023

Technol Cancer Res Treat

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Introduction: Pap smear is considered to be the primary examination for the diagnosis of cervical cancer. But the analysis of pap smear slides is a time-consuming task and tedious as it requires manual intervention. The diagnostic efficiency depends on the medical expertise of the pathologist, and human error often hinders the diagnosis. Automated segmentation and classification of cervical nuclei will help diagnose cervical cancer in earlier stages. Materials and Methods: The proposed methodology includes three models: a Residual-Squeeze-and-Excitation-module based segmentation model, a fusion-based feature extraction model, and a Multi-layer Perceptron classification model. In the fusion-based feature extraction model, three sets of deep features are extracted from these segmented nuclei using the pre-trained and fine-tuned VGG19, VGG-F, and CaffeNet models, and two hand-crafted descriptors, Bag-of-Features and Linear-Binary-Patterns, are extracted for each image. For this work, Herlev, SIPaKMeD, and ISBI2014 datasets are used for evaluation. The Herlev datasetis used for evaluating both segmentation and classification models. Whereas the SIPaKMeD and ISBI2014 are used for evaluating the classification model, and the segmentation model respectively. Results: The segmentation network enhanced the precision and ZSI by 2.04%, and 2.00% on the Herlev dataset, and the precision and recall by 0.68%, and 2.59% on the ISBI2014 dataset. The classification approach enhanced the accuracy, recall, and specificity by 0.59%, 0.47%, and 1.15% on the Herlev dataset, and by 0.02%, 0.15%, and 0.22% on the SIPaKMed dataset. Conclusion: The experiments demonstrate that the proposed work achieves promising performance on segmentation and classification in cervical cytopathology cell images..

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Section: Introductionmentioning

confidence: 99%

Nucleus segmentation and classification using residual SE-UNet and feature concatenation approach incervical cytopathology cell images

Chowdary

Suganya

Premalatha

et al. 2023

Technol Cancer Res Treat

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PM-LSMN: A Physical-Model-Based Lightweight Self-Attention Multiscale Net for Thin Cloud Removal

Zhao,

Zhou,

et al. 2024

IEEE Geosci. Remote Sensing Lett.

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Visual Place Recognition of Robots via Global Features of Scan-Context Descriptors with Dictionary-Based Coding

Kanji

2023

Applied Sciences

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Self-localization is a crucial requirement for visual robot place recognition. Particularly, the 3D point cloud obtained from 3D laser rangefinders (LRF) is applied to it. The critical part is the efficiency and accuracy of place recognition of visual robots based on the 3D point cloud. The current solution is converting the 3D point clouds to 2D images, and then processing these with a convolutional neural network (CNN) classification. Although the popular scan-context descriptor obtained from the 3D data can retain parts of the 3D point cloud characteristics, its accuracy is slightly low. This is because the scan-context image under the adjacent label inclines to be confusing. This study reclassifies the image according to the CNN global features through image feature extraction. In addition, the dictionary-based coding is leveraged to construct the retrieval dataset. The experiment was conducted on the North-Campus-Long-Term (NCLT) dataset under four-seasons conditions. The results show that the proposed method is superior compared to the other methods without real-time Global Positioning System (GPS) information.

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Class dependency-based learning using Bi-LSTM coupled with the transfer learning of VGG16 for the diagnosis of tuberculosis from chest X-rays

Cited by 3 publications

References 39 publications

Nucleus segmentation and classification using residual SE-UNet and feature concatenation approach incervical cytopathology cell images

Nucleus segmentation and classification using residual SE-UNet and feature concatenation approach incervical cytopathology cell images

PM-LSMN: A Physical-Model-Based Lightweight Self-Attention Multiscale Net for Thin Cloud Removal

Visual Place Recognition of Robots via Global Features of Scan-Context Descriptors with Dictionary-Based Coding

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