Toward development of a large field-of-view cancer screening patch (CASP) to detect cervical intraepithelial neoplasia

Gawedzinski, John; Schmeler, Kathleen M.; Milbourne, Andrea; Ramalingam, Preetha; Moghaddam, Parnian Ahmadi; Richards‐Kortum, Rebecca; Tkaczyk, Tomasz S.

doi:10.1364/boe.10.006145

Cited by 3 publications

(2 citation statements)

References 21 publications

(28 reference statements)

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“…Cropping cuts a portion of the input image. It is an effective tool for extracting patches [ 106 , 107 ]. Images can be classified according to patches rather than the whole image.…”

Section: Data Augmentationmentioning

confidence: 99%

A Survey on Deep Learning in COVID-19 Diagnosis

Han

Wang

et al. 2022

J. Imaging

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According to the World Health Organization statistics, as of 25 October 2022, there have been 625,248,843 confirmed cases of COVID-19, including 65,622,281 deaths worldwide. The spread and severity of COVID-19 are alarming. The economy and life of countries worldwide have been greatly affected. The rapid and accurate diagnosis of COVID-19 directly affects the spread of the virus and the degree of harm. Currently, the classification of chest X-ray or CT images based on artificial intelligence is an important method for COVID-19 diagnosis. It can assist doctors in making judgments and reduce the misdiagnosis rate. The convolutional neural network (CNN) is very popular in computer vision applications, such as applied to biological image segmentation, traffic sign recognition, face recognition, and other fields. It is one of the most widely used machine learning methods. This paper mainly introduces the latest deep learning methods and techniques for diagnosing COVID-19 using chest X-ray or CT images based on the convolutional neural network. It reviews the technology of CNN at various stages, such as rectified linear units, batch normalization, data augmentation, dropout, and so on. Several well-performing network architectures are explained in detail, such as AlexNet, ResNet, DenseNet, VGG, GoogleNet, etc. We analyzed and discussed the existing CNN automatic COVID-19 diagnosis systems from sensitivity, accuracy, precision, specificity, and F1 score. The systems use chest X-ray or CT images as datasets. Overall, CNN has essential value in COVID-19 diagnosis. All of them have good performance in the existing experiments. If expanding the datasets, adding GPU acceleration and data preprocessing techniques, and expanding the types of medical images, the performance of CNN will be further improved. This paper wishes to make contributions to future research.

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“…Cropping cuts a portion of the input image. It is an effective tool for extracting patches [ 106 , 107 ]. Images can be classified according to patches rather than the whole image.…”

Section: Data Augmentationmentioning

confidence: 99%

A Survey on Deep Learning in COVID-19 Diagnosis

Han

Wang

et al. 2022

J. Imaging

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“…Figure 18 Cropping. In traditional image processing and computer vision tasks, cropping is an efficient tool to extract patches from a large image or a mixed-size image set [264,265]. Then algorithms are run on the patches instead of the images themselves.…”

Section: Geometric Transformsmentioning

confidence: 99%

Advances in Data Preprocessing for Biomedical Data Fusion: An Overview of the Methods, Challenges, and Prospects

et al. 2021

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Development of a multimodal mobile colposcope for real-time cervical cancer detection

Coole¹,

Brenes²,

Possati-Resende

et al. 2022

Biomed. Opt. Express

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Cervical cancer remains a leading cause of cancer death among women in low-and middle-income countries. Globally, cervical cancer prevention programs are hampered by a lack of resources, infrastructure, and personnel. We describe a multimodal mobile colposcope (MMC) designed to diagnose precancerous cervical lesions at the point-of-care without the need for biopsy. The MMC integrates two complementary imaging systems: 1) a commercially available colposcope and 2) a high speed, high-resolution, fiber-optic microendoscope (HRME). Combining these two image modalities allows, for the first time, the ability to locate suspicious cervical lesions using widefield imaging and then to obtain co-registered high-resolution images across an entire lesion. The MMC overcomes limitations of high-resolution imaging alone; widefield imaging can be used to guide the placement of the high-resolution imaging probe at clinically suspicious regions and co-registered, mosaicked high-resolution images effectively increase the field of view of high-resolution imaging. Representative data collected from patients referred for colposcopy at Barretos Cancer Hospital in Brazil, including 22,800 high resolution images and 9,900 colposcope images, illustrate the ability of the MMC to identify abnormal cervical regions, image suspicious areas with subcellular resolution, and distinguish between high-grade and low-grade dysplasia.

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Toward development of a large field-of-view cancer screening patch (CASP) to detect cervical intraepithelial neoplasia

Cited by 3 publications

References 21 publications

A Survey on Deep Learning in COVID-19 Diagnosis

A Survey on Deep Learning in COVID-19 Diagnosis

Advances in Data Preprocessing for Biomedical Data Fusion: An Overview of the Methods, Challenges, and Prospects

Development of a multimodal mobile colposcope for real-time cervical cancer detection

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