Feature Extraction and Classification of Colon Cancer Using a Hybrid Approach of Supervised and Unsupervised Learning

Ghosh, Joydev; Sharma, Anil; Tomar, Sahil

doi:10.1007/978-3-030-71975-3_7

Cited by 1 publication

(2 citation statements)

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“…Modifications to the VGG-inspired CNN model ConvNet were evaluated by identifying colorectal cells, yielding values of 93.48%, 0.4385, 95.10%, and 92.76%, for accuracy, loss, sensitivity, and specificity, respectively [ 39 ]. Ghosh et al reported that the proposed classifiers had the highest classification accuracy (98.60%) among classifiers, ranging from 88.71% to 98.40% [ 51 ]. Furthermore, the diagnostic performance of AI and endoscopists yielded sensitivities of 97.30% and 87.40%, respectively, specificities of 99.00% and 96.40%, respectively, and processing times of 0.022 s/image and 2.4 s/image, respectively [ 46 ].…”

Section: Resultsmentioning

confidence: 99%

“…Ghosh et al developed a hybrid learning model that combined two machine learning techniques involving supervised (SL) and unsupervised learning techniques for the detection of colon cancer. This yielded better accuracy than existing approaches and could potentially be used for real-time cancer detection [ 51 ]. This study evaluated data clustering by K-means, the Girvan–Newman algorithm, and Mahalanobis distance-based clustering, followed by feature selection and dimensionality reduction based on principal component analysis.…”

Section: Methodological Approachesmentioning

confidence: 99%

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Deep Neural Network Models for Colon Cancer Screening

Kavitha

Gangadaran

Jackson

et al. 2022

Cancers

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Early detection of colorectal cancer can significantly facilitate clinicians’ decision-making and reduce their workload. This can be achieved using automatic systems with endoscopic and histological images. Recently, the success of deep learning has motivated the development of image- and video-based polyp identification and segmentation. Currently, most diagnostic colonoscopy rooms utilize artificial intelligence methods that are considered to perform well in predicting invasive cancer. Convolutional neural network-based architectures, together with image patches and preprocesses are often widely used. Furthermore, learning transfer and end-to-end learning techniques have been adopted for detection and localization tasks, which improve accuracy and reduce user dependence with limited datasets. However, explainable deep networks that provide transparency, interpretability, reliability, and fairness in clinical diagnostics are preferred. In this review, we summarize the latest advances in such models, with or without transparency, for the prediction of colorectal cancer and also address the knowledge gap in the upcoming technology.

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Section: Resultsmentioning

confidence: 99%

Section: Methodological Approachesmentioning

confidence: 99%