Early detection of breast cancer in histopathology images employing convolutional neural network (CNN)

Hilaliyah, Putri Khalifa; Irfan, M.; Lestandy, Merinda

doi:10.1063/5.0094608

Cited by 2 publications

(1 citation statement)

References 13 publications

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“…With the application of artificial intelligence (AI) methods, a new horizon opens for FNA and those new technologies can compensate partly for the lack of refined diagnostic methods in developing countries. A plethora of new technologies to improve image classification are available [16][17][18][19][20][21][22][23][24][25]. Image classification methods trained with neural networks make it possible to determine the probability of the classification of a FNA as either a benign or malignant breast disease [10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Artificial intelligence assisted diagnoses of fine-needle aspiration of breast diseases: a single-center experience

Fritz

Raoufi

Dalquen³

et al. 2023

JDH

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Purpose: Since 2010, physicians from Afghanistan have been uploading images of histological and cytological specimens to a telemedicine internet platform (iPath network) for expert evaluation. From this collective work, all cases with fine-needle aspirations (FNA) of mammary gland diseases were extracted and analyzed. The aim of the present retrospective feasibility study is to investigate the utility of artificial intelligence assisted diagnoses in fine-needle aspiration (FNA) of breast diseases.Material and Methods: A total of 3304 microphotographic images from 438 patients of smears from FNA of the mammary gland were available for this study. Telemedical expert diagnoses from 4 experienced cytopathologists were available in all 438 cases. Their diagnosis (malignant tumor of the mammary gland or benign mammary gland disease) was set as the gold standard. AI analysis was performed using i) clinical context data and ii) two different image recognition methods to determine the probability values for the presence of malignant breast tumor. Youden index and AUC (area under the curve) were used to evaluate test performance. Results: A score for invasive breast cancer (IBC) calculated from contextual variables agreed with the expert diagnosis (accuracy) in 85.2% and with the two image recognition systems in 78.4% and 65.2%. This simplifies health healthcare management of breast diseases in low income countries as in many patients the less expensive and less time-consuming technique of FNA may replace a histological examination.Conclusion: Image classification and analysis of context variables can be used to test the validity and plausibility of cytologic diagnoses, especially when cytologic interpretation has to be performed by people who are inexperienced in cytopathology.

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