Dataset Information

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 exhaustion 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: the Resnet-152 convolutional neural network architecture. Additionally, a region proposal network and a network combined with principal component analysis are 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 40,560 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.

SUBMITTER: Zhang J

PROVIDER: S-EPMC8121882 | biostudies-literature | 2021 May

REPOSITORIES: biostudies-literature

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Fast and accurate automated recognition of the dominant cells from fecal images based on Faster R-CNN.

Zhang Jing J Wang Xiangzhou X Ni Guangming G Liu Juanxiu J Hao Ruqian R Liu Lin L Liu Yong Y Du Xiaohui X Xu Fan F

Scientific reports 20210514 1

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 exhaustion time and accuracy remain major challenges in automatic testing. Here, we introduce a fast and efficient cell-detection algorithm based on the ...[more]

PMID: 33990662

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Project description:BACKGROUND:There are many different types of microRNAs (miRNAs) and elucidating their functions is still under intensive research. A fundamental step in functional annotation of a new miRNA is to classify it into characterized miRNA families, such as those in Rfam and miRBase. With the accumulation of annotated miRNAs, it becomes possible to use deep learning-based models to classify different types of miRNAs. In this work, we investigate several key issues associated with successful application of deep learning models for miRNA classification. First, as secondary structure conservation is a prominent feature for noncoding RNAs including miRNAs, we examine whether secondary structure-based encoding improves classification accuracy. Second, as there are many more non-miRNA sequences than miRNAs, instead of assigning a negative class for all non-miRNA sequences, we test whether using softmax output can distinguish in-distribution and out-of-distribution samples. Finally, we investigate whether deep learning models can correctly classify sequences from small miRNA families. RESULTS:We present our trained convolutional neural network (CNN) models for classifying miRNAs using different types of feature learning and encoding methods. In the first method, we explicitly encode the predicted secondary structure in a matrix. In the second method, we use only the primary sequence information and one-hot encoding matrix. In addition, in order to reject sequences that should not be classified into targeted miRNA families, we use a threshold derived from softmax layer to exclude out-of-distribution sequences, which is an important feature to make this model useful for real transcriptomic data. The comparison with the state-of-the-art ncRNA classification tools such as Infernal shows that our method can achieve comparable sensitivity and accuracy while being significantly faster. CONCLUSION:Automatic feature learning in CNN can lead to better classification accuracy and sensitivity for miRNA classification and annotation. The trained models and also associated codes are freely available at https://github.com/HubertTang/DeepMir.

Project description:BackgroundAlthough diagnostic ultrasound can non-invasively capture the image of abdominal viscera, diagnosis of the continuous ultrasound liver images to detect a liver tumor effectively and to determine whether the detected is benign or malignant is nontrivial. In order to minimize the gaps in diagnostic accuracy depending on doctor's proficiency, we built an automated system to support the ultrasonography of liver tumors by employing deep learning technologies.MethodsWe constructed a neural network model for the automated detection of tumor tissues and blood vessels from the sequential liver ultrasound images. Faster region-based convolutional neural networks (Faster R-CNN) is employed as a base model for the object detection, which can output the detection results in 4 frames per second and enable the system to be particularly suitable for the real time ultrasonography. Moreover, we proposed a new neural network architecture feeding both the current and previous images into Faster R-CNN. For training the models, intraoperative ultrasound images obtained from one hepatocellular carcinoma (HCC) patient were used. The obtained image was a multifaceted observation of the liver and includes one HCC and some blood vessels. We labeled 91 images with the help of a liver specialist. We compared the tumor detection performance of the plain Faster R-CNN model with that of the proposed model.ResultsWe find that both the models performed well in detecting HCC and blood vessels, after training with 400 epochs using Adam. However, the mean precision of our model reaches 0.549, which is 0.019 better than that of the plain Faster R-CNN, and the mean sensitivity of our model about HCC reaches 0.623±0.385 for 30 scenes of sequential liver ultrasound images, which is also 0.146 better than that of the plain Faster R-CNN model.ConclusionsThe comparison between the proposed model and the plain Faster R-CNN model shows that we achieved better accuracy in tumor detection, in terms of the mean precision as well as the mean sensitivity, with the proposed model.

Dataset Information

Fast and accurate automated recognition of the dominant cells from fecal images based on Faster R-CNN.

Publications

Fast and accurate automated recognition of the dominant cells from fecal images based on Faster R-CNN.

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