Feature assisted cervical cancer screening through DIC cell images

Adhikary, Shreya; Seth, Sayan; Das, Seema; Naskar, Tapan Kumar; Barui, Ananya; Maity, Santi P.

doi:10.1016/j.bbe.2021.07.005

Cited by 6 publications

(4 citation statements)

References 31 publications

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“…Former studies have successfully used a combination of DL and image processing algorithms to achieve cell segmentation from DIC microscopic images, with average IoU values ranging from 0.85 to 0.9 [ [61] , [62] , [63] ]. However, the majority of these algorithms were specifically developed for segmenting round-shaped cells, resulting in contour segments characterized by smooth boundaries.…”

Section: Discussionmentioning

confidence: 99%

A deep learning-based pipeline for analyzing the influences of interfacial mechanochemical microenvironments on spheroid invasion using differential interference contrast microscopic images

Ngo,

Yang,

Mao

et al. 2023

Materials Today Bio

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Section: Discussionmentioning

confidence: 99%

A deep learning-based pipeline for analyzing the influences of interfacial mechanochemical microenvironments on spheroid invasion using differential interference contrast microscopic images

Ngo,

Yang,

Mao

et al. 2023

Materials Today Bio

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“…Adhikary, Shreya et al. ( 17 ) tackled the differential interference contrast (DIC) dataset, employing classification techniques such as multilayer perceptron (MLP), SVM, and k-NN after cell segmentation using a modified valley-linked Otsu’s threshold approach. Principal component analysis (PCA) was also utilized to select features and enhance classifier performance.…”

Section: Related Workmentioning

confidence: 99%

HO-SsNF: heap optimizer-based self-systematized neural fuzzy approach for cervical cancer classification using pap smear images

Shanmugam,

KVN,

Radhabai

et al. 2024

Front. Oncol.

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Cervical cancer is a significant concern for women, necessitating early detection and precise treatment. Conventional cytological methods often fall short in early diagnosis. The proposed innovative Heap Optimizer-based Self-Systematized Neural Fuzzy (HO-SsNF) method offers a viable solution. It utilizes HO-based segmentation, extracting features via Gray-Level Co-Occurrence Matrix (GLCM) and Local Binary Pattern (LBP). The proposed SsNF-based classifier achieves an impressive 99.6% accuracy in classifying cervical cancer cells, using the Herlev Pap Smear database. Comparative analyses underscore its superiority, establishing it as a valuable tool for precise cervical cancer detection. This algorithm has been seamlessly integrated into cervical cancer diagnosis centers, accessible through smartphone applications, with minimal resource demands. The resulting insights provide a foundation for advancing cancer prevention methods.

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“…One category is i k -th and the other is the rest of (M − s + 1) categories except the ik -th. The classification precision is denoted as P (M−s + 1)k, the total number of feature subset is denoted as q, and the objective functions of serial SVM multi-class classifier are given by the following algorithm [40]:…”

Section: Support Vector Machinementioning

confidence: 99%

“…Thus, the s-th level is designed to reach the highest classification precision and the least number of feature subsets. The samples are separated into two parts; training and determining the least number of features during the feature subset selection process and classifiers construction purposed to evaluate the selected features [40].…”

Section: Support Vector Machinementioning

confidence: 99%

Cervical Cancer Classification Using Combined Machine Learning and Deep Learning Approach

Alquran¹,

Mustafa²,

Qasmieh³

et al. 2022

Computers, Materials &Amp; Continua

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Cervical cancer is screened by pap smear methodology for detection and classification purposes. Pap smear images of the cervical region are employed to detect and classify the abnormality of cervical tissues. In this paper, we proposed the first system that it ables to classify the pap smear images into a seven classes problem. Pap smear images are exploited to design a computer-aided diagnoses system to classify the abnormality in cervical images cells. Automated features that have been extracted using ResNet101 are employed to discriminate seven classes of images in Support Vector Machine (SVM) classifier. The success of this proposed system in distinguishing between the levels of normal cases with 100% accuracy and 100% sensitivity. On top of that, it can distinguish between normal and abnormal cases with an accuracy of 100%. The high level of abnormality is then studied and classified with a high accuracy. On the other hand, the low level of abnormality is studied separately and classified into two classes, mild and moderate dysplasia, with ∼ 92% accuracy. The proposed system is a built-in cascading manner with five models of polynomial (SVM) classifier. The overall accuracy in training for all cases is 100%, while the overall test for all seven classes is around 92% in the test phase and overall accuracy reaches 97.3%. The proposed system facilitates the process of detection and classification of cervical cells in pap smear images and leads to early diagnosis of cervical cancer, which may lead to an increase in the survival rate in women.

show abstract

Feature assisted cervical cancer screening through DIC cell images

Cited by 6 publications

References 31 publications

A deep learning-based pipeline for analyzing the influences of interfacial mechanochemical microenvironments on spheroid invasion using differential interference contrast microscopic images

A deep learning-based pipeline for analyzing the influences of interfacial mechanochemical microenvironments on spheroid invasion using differential interference contrast microscopic images

HO-SsNF: heap optimizer-based self-systematized neural fuzzy approach for cervical cancer classification using pap smear images

Cervical Cancer Classification Using Combined Machine Learning and Deep Learning Approach

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