Convolutional Neural Network Based Localized Classification of Uterine Cervical Cancer Digital Histology Images.

Almubarak, Haidar; Stanley, R. Joe; Long, Rodney; Thoma, George R.; Frazier, Shelliane R.

doi:10.1016/j.procs.2017.09.044

Cited by 41 publications

(24 citation statements)

References 10 publications

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“…Because ROSE typically requires the presence of a skilled pathologist, the integration of innovations such as telepathology or the development of a mobile phone application that would automate classification of the FNAB specimens, similar to those already available for melanoma and cervical cancer, would help to increase the availability of pathology services. 32 , 33 Moreover, the integration of radiologists and systematic adoption Breast Imaging-Reporting and Data System reporting will also be important for developing resource-appropriate early detection programs and further refine who truly needs referral for additional cancer services.…”

Section: Discussionmentioning

confidence: 99%

“…Although using CBE as the primary method to screen for breast cancer may miss smaller lesions, and the addition of ultrasound would increase sensitivity, false positives are more common with ultrasound screening and would possibly lead to unnecessary procedures and increased resource utilization. 33 High-resolution ultrasound needed for breast cancer screening is also cost-prohibitive and not widely available in many resource-constrained settings, including Tanzania. In addition, there have been no studies demonstrating a difference in mortality benefit between using CBE and FNAB versus US and FNAB in low-resource settings.…”

Section: Discussionmentioning

confidence: 99%

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Feasibility of Fine-Needle Aspiration Biopsy and Rapid On-Site Evaluation for Immediate Triage in Breast Cancer Screening in Tanzania

Philipo

Vuhahula

Kimambo

et al. 2021

JCO Global Oncology

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PURPOSE Clinical breast examination (CBE) is one of the most common methods used for early detection of breast cancer in low- and middle-income countries. CBE alone is limited by lack of specificity and may result in unnecessary diagnostic procedures. We evaluated the feasibility of integrating CBE, fine-needle aspiration biopsy (FNAB), and rapid on-site evaluation (ROSE) in triaging palpable breast masses for specialized cancer care. MATERIALS AND METHODS An intensive breast cancer screening event was conducted at a national trade fair by a multidisciplinary team of care providers targeting a healthy population in Dar es Salaam, Tanzania. All adults age ≥ 18 years were invited to participate. CBE was performed by oncologists and/or pathologists. FNAB was performed by a pathologist on palpable masses that were then categorized as benign, indeterminate, or suspicious for malignancy or definitively malignant based on ROSE. RESULTS A total of 208 individuals (207 females, one male; median age, 36 years; range, 18-68 years) were screened. Most (90.8%, 189 of 208) had normal findings, whereas 7.2% (15 of 208), 1% (2 of 208), and 1% (2 of 208) had a palpable mass, breast pain, and nipple discharge, respectively. Two participants had lesions too small for palpation-guided biopsy and clinically consistent with fibroadenomas; the participants were counseled, and observation was recommended. FNAB was performed on 13 breast masses, with 9 of 13 (69%) categorized as benign and 4 of 13 (31%) suspicious for malignancy. Final cytopathologic review of referred patients confirmed one case to be breast adenocarcinoma, one was suggestive of fibroadenoma, and two showed inflammations. CONCLUSION Integration of CBE with ROSE and FNAB was feasible in a breast cancer screening program in Tanzania. In settings with constrained resources for cancer care, this may be an effective method for triaging patients with breast masses.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Feasibility of Fine-Needle Aspiration Biopsy and Rapid On-Site Evaluation for Immediate Triage in Breast Cancer Screening in Tanzania

Philipo

Vuhahula

Kimambo

et al. 2021

JCO Global Oncology

View full text Add to dashboard Cite

show abstract

“…There are several techniques for computer-aided diagnosis of cervical cancer [ 7 , 8 , 9 , 10 ]. Most of them use cytological images [ 11 , 12 , 13 , 14 , 15 , 16 , 17 ]. However, an increasing number of studies are developing methods for automatic classification of images captured during VIA, and often, adding images taken during the visual inspection with Lugol’s iodine (VILI) or with the green lens.…”

Section: Introductionmentioning

confidence: 99%

Using Dynamic Features for Automatic Cervical Precancer Detection

et al. 2021

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Cervical cancer remains a major public health concern in developing countries due to financial and human resource constraints. Visual inspection with acetic acid (VIA) of the cervix was widely promoted and routinely used as a low-cost primary screening test in low- and middle-income countries. It can be performed by a variety of health workers and the result is immediate. VIA provides a transient whitening effect which appears and disappears differently in precancerous and cancerous lesions, as compared to benign conditions. Colposcopes are often used during VIA to magnify the view of the cervix and allow clinicians to visually assess it. However, this assessment is generally subjective and unreliable even for experienced clinicians. Computer-aided techniques may improve the accuracy of VIA diagnosis and be an important determinant in the promotion of cervical cancer screening. This work proposes a smartphone-based solution that automatically detects cervical precancer from the dynamic features extracted from videos taken during VIA. The proposed solution achieves a sensitivity and specificity of 0.9 and 0.87 respectively, and could be a solution for screening in countries that suffer from the lack of expensive tools such as colposcopes and well-trained clinicians.

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“…Author also proposed a gene forward neural network combining genetic algorithms and feed forward neural network for detection of cervical cancer. In [78], Haidar et al used CNN to classify the epithelium into four classes according to CIN classification model. Author used three CNNs for classification purpose.…”

Section: Computational Complexitymentioning

confidence: 99%

A Review on Automated Cancer Detection in Medical Images using Machine Learning and Deep Learning based Computational Techniques: Challenges and Opportunities

Manhas

Gupta

Roy

2021

Arch Computat Methods Eng

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Cancer is one of the most deadly diseases diagnosed among the population across the globe so far. The number of cases is increasing at a high pace each year that subsequently leads to the advancement in different diagnosis tools and technologies to handle this pandemic. Significant increase in the mortality rate worldwide leads tremendous scope to device and implement latest computer aided diagnostic systems for its early detection. The one among such techniques is machine learning coupled with medical imaging modalities. This combination has proven to be efficient in diagnosing various medical conditions in cancer diagnosis. Current study presents a review of different machine learning techniques applied on imaging modalities for cancer diagnosis from 2008 to 2019. This study focuses on diagnosis of five most prevalent and deadly cancers i.e., cervical cancer, oral cancer, breast cancer, brain cancer and skin cancer. Extensive and exhaustive review was carried out after going through different research papers, research articles and book chapters published by reputed international and national publishers such as Springer Link, Science Direct, IEEE Xplore Digital library and PubMed. A number of conference proceedings have also been included subject to the fulfilling of our quality evaluation criteria. This review article provides a comprehensive overview of machine learning approaches using image modalities for cancer detection and diagnosis with main focus on challenges being faced during their research. Majority of the challenges are identified based on the use of potential machine learning based approaches, image modalities, features and evaluation metrics. This review not only identified challenges but also ear mark and present the new research opportunities for researchers working in this field. It has been widely observed that traditional machine learning algorithms Like SVM, GMM performed excellent in classification whereas the deep learning has dominated the field of medical image analysis to a greater extent. It is evident from the literature survey that the researchers have achieved the accuracies of 100% in classification of cancerous and normal tissue images using different machine learning techniques. This article will provide an insight to the researchers working in this domain to identify which machine learning technique work best on what type of data set, selection of features, various challenges and their proposed solutions in solving this complex problem. Limitations and future research opportunities in the field of implementing different machine learning techniques in cancer diagnosis and classification is also presented at the end of this review article.

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Convolutional Neural Network Based Localized Classification of Uterine Cervical Cancer Digital Histology Images.

Cited by 41 publications

References 10 publications

Feasibility of Fine-Needle Aspiration Biopsy and Rapid On-Site Evaluation for Immediate Triage in Breast Cancer Screening in Tanzania

Feasibility of Fine-Needle Aspiration Biopsy and Rapid On-Site Evaluation for Immediate Triage in Breast Cancer Screening in Tanzania

Using Dynamic Features for Automatic Cervical Precancer Detection

A Review on Automated Cancer Detection in Medical Images using Machine Learning and Deep Learning based Computational Techniques: Challenges and Opportunities

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