Fine-Grained Classification of Cervical Cells Using Morphological and Appearance Based Convolutional Neural Networks

Lin, Haoming; Hu, Yuyang; Chen, Siping; Yao, Jianhua; Zhang, Ling

doi:10.1109/access.2019.2919390

Cited by 92 publications

(50 citation statements)

References 40 publications

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“…On the other hand, studies [99], [116]- [119], [121] that combined CNN with SVM, transfer learning, or decision tree performed out of the box for the classification of cervical cancer with a little or no preprocessing. A combination of CNN (AlexNet) along with transfer learning and decision tree-based algorithm [121] provides superior performance over others [99], [116]- [119], [122]- [125]. AlexNet is considering to have a smaller architecture with efficient performance.…”

Section: B Analysis Of the Classification Methodsmentioning

confidence: 99%

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A Survey for Cervical Cytopathology Image Analysis Using Deep Learning

Rahaman

et al. 2020

IEEE Access

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Cervical cancer is one of the most common and deadliest cancers among women. Despite that, this cancer is entirely treatable if it is detected at a precancerous stage. Pap smear test is the most extensively performed screening method for early detection of cervical cancer. However, this hand-operated screening approach suffers from a high false-positive result because of human errors. To improve the accuracy and manual screening practice, computer-aided diagnosis methods based on deep learning is developed widely to segment and classify the cervical cytology images automatically. In this survey, we provide a comprehensive study of the state of the art approaches based on deep learning for the analysis of cervical cytology images. Firstly, we introduce deep learning and its simplified architectures that have been used in this field. Secondly, we discuss the publicly available cervical cytopathology datasets and evaluation metrics for segmentation and classification tasks. Then, a thorough review of the recent development of deep learning for the segmentation and classification of cervical cytology images is presented. Finally, we investigate the existing methodology along with the most suitable techniques for the analysis of pap smear cells.

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Section: B Analysis Of the Classification Methodsmentioning

confidence: 99%

“…In [122], a unique cervical cell study is presented by connecting morphological and appearance-based characteristics with in-depth features. They examined that the combination of appearance and morphology-based CNNs provides more positive classification accuracy than their individual.…”

Section: A Reference Reviewmentioning

confidence: 99%

A Survey for Cervical Cytopathology Image Analysis Using Deep Learning

Rahaman

et al. 2020

IEEE Access

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“…They are defined in Equations (2)-(4), and the definition of the classification metrics is shown in Table 4. Moreover, the following are the definitions of TP, TN, FN, and FP: TP is the number of correctly classified images with the red light, TN is the number of correctly classified images with the non-red light, FN is the number of falsely classified images with the red light, and FP is the number of falsely classified images with the non-red light [48,49]. To validate the performance of the training process, two indicators of training accuracy and loss function are used to evaluate the results of the self-predicted data.…”

Section: Performance Of the Resnet-50 Classification Modelmentioning

confidence: 99%

Automated Classification of Blood Loss from Transurethral Resection of the Prostate Surgery Videos Using Deep Learning Technique

Chen

Lin

et al. 2020

Applied Sciences

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Transurethral resection of the prostate (TURP) is a surgical removal of obstructing prostate tissue. The total bleeding area is used to determine the performance of the TURP surgery. Although the traditional method for the detection of bleeding areas provides accurate results, it cannot detect them in time for surgery diagnosis. Moreover, it is easily disturbed to judge bleeding areas for experienced physicians because a red light pattern arising from the surgical cutting loop often appears on the images. Recently, the automatic computer-aided technique and artificial intelligence deep learning are broadly used in medical image recognition, which can effectively extract the desired features to reduce the burden of physicians and increase the accuracy of diagnosis. In this study, we integrated two state-of-the-art deep learning techniques for recognizing and extracting the red light areas arising from the cutting loop in the TURP surgery. First, the ResNet-50 model was used to recognize the red light pattern appearing in the chipped frames of the surgery videos. Then, the proposed Res-Unet model was used to segment the areas with the red light pattern and remove these areas. Finally, the hue, saturation, and value color space were used to classify the four levels of the blood loss under the circumstances of non-red light pattern images. The experiments have shown that the proposed Res-Unet model achieves higher accuracy than other segmentation algorithms in classifying the images with the red and non-red lights, and is able to extract the red light patterns and effectively remove them in the TURP surgery images. The proposed approaches presented here are capable of obtaining the level classifications of blood loss, which are helpful for physicians in diagnosis.

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“…Cervical cancer is one of the four common cancers in the world. Approximately 266,000 people die of cervical cancer each year [1], [2]. However, the early canceration of cervical cells has no visible symptoms of physical malaise or physiological reaction that are easily perceptible.…”

Section: Introductionmentioning

confidence: 99%

Nucleus Segmentation of Cervical Cytology Images Based on Depth Information

et al. 2020

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In cervical cancer screening, accurate segmentation of cervical nucleus is a key part in the early diagnosis of cervical cancer. However, the cervical nucleus segmentation faces many great challenges owing to the overlapping cervical cells, uneven staining and poor contrast of cervical cytology smear images. In this paper, a tree domain structure and screening algorithm based on depth-first searching strategy are proposed to obtain candidate nucleus regions according to the annular clustering characteristics of nucleus depth information in cervical cytology images. Then, the candidate nucleus regions are finely segmented with an iterative level set algorithm based on adaptive radius morphological dilation. Experimental results are evaluated on the ISBI2015 public dataset. The performance of the proposed nucleus segmentation algorithm is higher than that of the state-of-the-art methods in terms of positive predictive value, negative predictive value, precision, recall of the cervical nucleus segmentation. INDEX TERMS Cervical cancer screening, cervical cell, nucleus segmentation, tree domain structure.

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Fine-Grained Classification of Cervical Cells Using Morphological and Appearance Based Convolutional Neural Networks

Cited by 92 publications

References 40 publications

A Survey for Cervical Cytopathology Image Analysis Using Deep Learning

A Survey for Cervical Cytopathology Image Analysis Using Deep Learning

Automated Classification of Blood Loss from Transurethral Resection of the Prostate Surgery Videos Using Deep Learning Technique

Nucleus Segmentation of Cervical Cytology Images Based on Depth Information

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