Heuristic neural network approach in histological sections detection of hydatidiform mole

Abstract

A heuristic-based, multineural network (MNN) image analysis as a solution to the problematical diagnosis of hydatidiform mole (HM) is presented. HM presents as tumors in placental cell structures, many of which exhibit premalignant phenotypes (choriocarcinoma and other conditions). HM is commonly found in women under age 17 or over 35 and can be partial HM or complete HM. Appropriate treatment is determined by correct categorization into PHM or CHM, a difficult task even for expert pathologists. Image analysis combined with pattern recognition techniques has been applied to the problem, based on 15 or 17 image features. The use of limited data for training and validation set was optimized using a k-fold validation technique allowing performance measurement of different MNN configurations. The MNN technique performed better than human experts at the categorization for both the 15-and 17-feature data, promising greater diagnostic consistency, and further improvements with the availability of larger datasets.

[Retracted] Design and Research of the AI Badminton Model Based on the Deep Learning Neural Network

“…MNN is a lightweight inference engine based on the deep neutral network (DNN), and loaded on its end side are DNN models. Up to now, MNN has seen wide applications in face detection, gesture recognition, portrait segmentation, and other things [11].…”

Segmentation Network with Compound Loss Function for Hydatidiform Mole Hydrops Lesion Recognition

“…Neural network already has preliminary results in pathologic diagnosis of hydatidiform mole. P. Pal et al [3] classified pathological sections of hydatidiform mole villi into normal, PHM, or CHM categories based on various characteristics of hydatidiform mole using three fully connected networks. The overall accuracy of the validation dataset is able to reach 86.1%.…”

“…H YDATIDIFORM mole(HM) is one of the most common gestational trophoblastic diseases(GTD), which occur in about 1 in 500-1000 pregnancies [1]- [2]. Since there is a certain probability that HM will develop into invasive HM and choriocarcinoma, most HM fetuses are unviable, or HM grows into a teratoma [3]- [6]. HM is commonly found in women under age 17 or over age 35, and can be partial or complete.…”

“…An image semantic segmentation network based on deep learning has been proved to effectively increase the accuracy and efficiency of diagnosis in many medical imaging lesion segmentation tasks. However, there is rare research on intelligent diagnosis of HM lesions based on deep learning [3]. To address the above issues, we propose an intelligent auxiliary diagnosis method that can identify HM lesions under a microscope in real time.…”