CNN-based Approach for Cervical Cancer Classification in Whole-Slide Histopathology Images

Idlahcen, Ferdaous; Himmi, Mohammed Majid; Mahmoudi, Abdelhak

doi:10.48550/arxiv.2005.13924

Cited by 3 publications

(2 citation statements)

References 7 publications

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“…CNNs offer promising prospects for the enhancement of histopathological image classification systems in breast cancer diagnosis. This advancement promises to significantly reduce the diagnostic time while delivering impressive outcomes more swiftly [5][6][7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Chasing a Better Decision Margin for Discriminative Histopathological Breast Cancer Image Classification

Alirezazadeh,

Dornaika,

Moujahid

2023

Electronics

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When considering a large dataset of histopathologic breast images captured at various magnification levels, the process of distinguishing between benign and malignant cancer from these images can be time-intensive. The automation of histopathological breast cancer image classification holds significant promise for expediting pathology diagnoses and reducing the analysis time. Convolutional neural networks (CNNs) have recently gained traction for their ability to more accurately classify histopathological breast cancer images. CNNs excel at extracting distinctive features that emphasize semantic information. However, traditional CNNs employing the softmax loss function often struggle to achieve the necessary discriminatory power for this task. To address this challenge, a set of angular margin-based softmax loss functions have emerged, including angular softmax (A-Softmax), large margin cosine loss (CosFace), and additive angular margin (ArcFace), each sharing a common objective: maximizing inter-class variation while minimizing intra-class variation. This study delves into these three loss functions and their potential to extract distinguishing features while expanding the decision boundary between classes. Rigorous experimentation on a well-established histopathological breast cancer image dataset, BreakHis, has been conducted. As per the results, it is evident that CosFace focuses on augmenting the differences between classes, while A-Softmax and ArcFace tend to emphasize augmenting within-class variations. These observations underscore the efficacy of margin penalties on angular softmax losses in enhancing feature discrimination within the embedding space. These loss functions consistently outperform softmax-based techniques, either by widening the gaps among classes or enhancing the compactness of individual classes.

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Section: Introductionmentioning

confidence: 99%

Chasing a Better Decision Margin for Discriminative Histopathological Breast Cancer Image Classification

Alirezazadeh,

Dornaika,

Moujahid

2023

Electronics

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“…Over the last few years, CNN have had good achievements to detect the cancerous cells on Pap smear images [10][11][12][13][14][15]. AlexNet, VGG-16, and RestNet are the architecture of CNN that are most often used to perform segmentation and classification tasks in cervical cancer [16][17][18]. The combination of CNN and Support Vector Machine (SVM) was implemented to classify pap smear images that results accuracy > 90% [19][20][21][22].…”

Section: Introductionmentioning

confidence: 99%

Layer Selection on Residual Network for Feature Extraction of Pap Smear Images

Alfian Hamam Akbar,

Imas Sukaesih Sitanggang,

Muhammad Asyhar Agmalaro

et al. 2023

ARASET

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Pap smear screening test is one of the early prevention efforts to detect cervical cancer. Manual screening tests are still prone to observation errors. This study aims to create a convolutional neural network (CNN) model and support vector machine (SVM) model to identify cervical cancer through pap smear images. The data used are 4049 normal and pathological cervical cells in pap smear images sourced from SIPaKMeD, which were divided into 5 classes based on the level of cancer malignancy. The CNN model is used to extract features on the pap smear image, and SVM is used to carry out the classification. The results of this study are four cervical cancer classification models on pap smear images using Resnet50 and Resnet50V2 architecture and SVM algorithms with different scenarios on freeze and unfreeze of the convolution layer. The classification model with the best performance has an accuracy of 97.09%. CNN model with freezing the convolution layer provides much faster in the pre-trained model and the integration of this model with the SVM as the classifier results in the classification model of cervical cells in pap smear images with high accuracy.

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Precise Identification of Different Cervical Intraepithelial Neoplasia (CIN) Stages, Using Biomedical Engineering Combined with Data Mining and Machine Learning

Kruczkowski,

Drabik-Kruczkowska,

Wesołowski

et al. 2024

Interdisciplinary Cancer Research

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CNN-based Approach for Cervical Cancer Classification in Whole-Slide Histopathology Images

Cited by 3 publications

References 7 publications

Chasing a Better Decision Margin for Discriminative Histopathological Breast Cancer Image Classification

Chasing a Better Decision Margin for Discriminative Histopathological Breast Cancer Image Classification

Layer Selection on Residual Network for Feature Extraction of Pap Smear Images

Precise Identification of Different Cervical Intraepithelial Neoplasia (CIN) Stages, Using Biomedical Engineering Combined with Data Mining and Machine Learning

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