A hybrid model for efficient cervical cell classification

Sabeena, K; Gopakumar, C.

doi:10.1016/j.bspc.2021.103288

Cited by 10 publications

(3 citation statements)

References 38 publications

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“…Win et al [11] , Rehman et al [12] and Sabeena and Gopakumar [13] applied deep learning algorithms followed by machine learning techniques to classify cervical cancer cells. On the other hand, [11] employed a bagging ensemble classifier that computes the output of base learners during the classification stage.…”

Section: Background and Literature Reviewmentioning

confidence: 99%

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Mask region-based CNNs for cervical cancer progression diagnosis on pap smear examinations

Rutili de Lima,

Khan,

Shah

et al. 2023

Heliyon

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Section: Background and Literature Reviewmentioning

confidence: 99%

“…On the other hand, [11] employed a bagging ensemble classifier that computes the output of base learners during the classification stage. Moreover, Rehman et al [12] and Sabeena and Gopakumar [13] tested each model separately at the end.…”

Section: Background and Literature Reviewmentioning

confidence: 99%

Mask region-based CNNs for cervical cancer progression diagnosis on pap smear examinations

Rutili de Lima,

Khan,

Shah

et al. 2023

Heliyon

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“…Siyuan Lu et al 18,19 effectively utilized transfer learning techniques to obtain the image-level representation based on CNN as the backbone for automatically identifying COVID-19 in chest CT images and experimented with batch normalized AlexNet and chaotic bat algorithm for optimizing classification performance in the process of detecting abnormal brain from MRI. Sabeena et al 20,21 investigated the performance ensemble-based stack classifier and unification of wavelet transform and CNN for efficient cervical cell classification and found that the CNN-based segmentation followed by ensemble-based stack classifier achieved the best performance for cervical cell classification. Chinnu et al 22 used Blind/Referenceless Image Spatial Quality Evaluator-based approach to extract the slices having lung abnormalities from the dataset followed by a novel shallow CNN model to detect lung carcinoma from multimodality images.…”

mentioning

confidence: 99%

Attention augmented residual autoencoder for efficient polyp segmentation

Sushama

Menon

2022

Int J Imaging Syst Tech

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Colon cancer has been reported to be one of the frequently diagnosed cancers and the leading cause of cancer deaths. Early detection and removal of malicious polyps, which are precursors of colon cancer, can enormously lessen the fatality rate. The detection and segmentation of polyps in colonoscopy is a challenging task even for an experienced colonoscopist, due to divergences in the size, shape, texture, and the close resemblance of polyps with the colon lining. Machine‐assisted detection, localization, and segmentation of polyps in the screening procedure can profoundly help the clinicians. Autoencoder‐based architectures used in polyp segmentation lack the efficiency in incorporating both local and long‐range pixel dependencies. To address the challenges in the automatic segmentation of colon polyps we propose an autoencoder architecture, augmented with a feature attention module in the decoder part. The salient features from RGB colonoscopic images are extracted using the residual skip‐connected autoencoder. The decoder attention module joins spatial subspace with feature subspace extracted from the deep residual convolutional neural network and enhances the feature weight for precise segmentation of polyp regions. Extensive experiments on four publicly available polyp datasets demonstrate that the proposed architecture provides very impressive performance in terms of segmentation metrics (Dice scores and Jaccard scores) when compared with the state‐of‐the‐art polyp segmentation approaches.

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