Fast and Accurate Automated Recognition of the Dominant Cells From Fecal Images Based on Faster R-CNN

Abstract

Fecal samples can easily be collected and are representative of a person’s current health state; therefore, the demand for routine fecal examination has increased sharply. However, manual operation may pollute the samples, and low efficiency limits the general examination speed; therefore, automatic analysis is needed. Nevertheless, recognition exhaust time and accuracy remain major challenges in automatic testing. Here, we introduce a fast and efficient cell detection algorithm based on the Faster-R-CNN technique and is the Resnet-152 convolutional neural network architecture. Additionally, a region proposal network and a network combined with principal component analysis were proposed for cell location and recognition in microscopic images. Our algorithm achieved a mean average precision of 84% and a 723 ms detection time per sample for 40560 fecal images. Thus, this approach may provide a solid theoretical basis for real-time detection in routine clinical examinations while accelerating the process to satisfy increasing demand.

End-to-End Deep Learning-Based Cells Detection in Microscopic Leucorrhea Images

“…In recent years, the rise of deep learning provides an end-to-end solution for automatic cell detection tasks (Wang et al, 2018;Zhang et al, 2021). There are currently two types of well-known representative deep learning-based object detection patterns: the two-stage detection framework and the single-stage detection framework (Soviany & Ionescu, 2019).…”

ICIP 2022 Challenge: PEDCMI, TOOD Enhanced by Slicing-Aided Fine-Tuning and Inference

“…Although this model was presented in 2015, due to its simplicity, easy modularity, and stability during a training process, it is one of the most practically implemented object detection models across diverse application areas, ranging from robotic plant harvesting [2,3] to medical image analysis [4]. Even the recent work of [5] applied the Faster R-CNN model to detect red blood cells (RBCs), white blood cells (WBCs), pyocytes (PYOs), and mildews (Mids) in microscopic images of fecal samples.…”