Intelligent Solutions in Chest Abnormality Detection Based on YOLOv5 and ResNet50

Yu, Luo; Zhang, Yifan; Sun, Xize; Dai, Hengwei; Chen, Xiaohui

doi:10.1155/2021/2267635

Cited by 41 publications

(24 citation statements)

References 51 publications

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“…They are characterized by two-stage methods. Another approach is to construct an end-to-end model to directly obtain the final detection result, such as the You-Only-Look-Once (YOLO) series [8, 9]. YOLO neural network design predicts a collection of bounding boxes, confidence scores, and class probabilities.…”

Section: Introductionmentioning

confidence: 99%

Relationship between Liquid-Based Cytology Preservative Solutions and Artificial Intelligence: Liquid-Based Cytology Specimen Cell Detection Using YOLOv5 Deep Convolutional Neural Network

Ikeda¹,

Sakabe²,

Maruyama³

et al. 2022

Acta Cytologica

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Introduction: Deep learning is a subset of machine learning that has contributed to significant changes in feature extraction and image classification and is being actively researched and developed in the field of cytopathology. Liquid-based cytology (LBC) enables standardized cytological preparation and is also applied to artificial intelligence (AI) research, but cytological features differ depending on the LBC preservative solution types. In this study, the relationship between cell detection by AI and the type of preservative solution used was examined. Methods: The specimens were prepared from five preservative solutions of LBC and stained using the Papanicolaou method. The YOLOv5 deep convolutional neural network algorithm was used to create a deep learning model for each specimen, and a BRCPT model from five specimens was also created. Each model was compared to the specimen types used for detection. Results: Among the six models, a difference in the detection rate of approximately 25% was observed depending on the detected specimen, and within specimens, a difference in the detection rate of approximately 20% was observed depending on the model. The BRCPT model had little variation in the detection rate depending on the type of the detected specimen. Conclusions: The same cells were treated with different preservative solutions, the cytologic features were different, and AI clarified the difference in cytologic features depending on the type of solution. The type of preservative solution used for training and detection had an extreme influence on cell detection using AI. Although the accuracy of the deep learning model is important, it is necessary to understand that cell morphology differs depending on the type of preservative solution, which is a factor affecting the detection rate of AI.

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Section: Introductionmentioning

confidence: 99%

Relationship between Liquid-Based Cytology Preservative Solutions and Artificial Intelligence: Liquid-Based Cytology Specimen Cell Detection Using YOLOv5 Deep Convolutional Neural Network

Ikeda¹,

Sakabe²,

Maruyama³

et al. 2022

Acta Cytologica

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“…There are very few papers published that present methods for object detection in chest X-ray images. One notable paper similar to our work is the one by Luo et al [2]. They use the same dataset as the experiments presented in this paper and train a YOLOv5 model with a ResNet50-based backbone.…”

Section: A Related Workmentioning

confidence: 91%

Self-Supervised Learning as a Means To Reduce the Need for Labeled Data in Medical Image Analysis

Benčević¹,

Habijan²,

Galić³

et al. 2022

Preprint

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One of the largest problems in medical image processing is the lack of annotated data. Labeling medical images often requires highly trained experts and can be a time-consuming process. In this paper, we evaluate a method of reducing the need for labeled data in medical image object detection by using self-supervised neural network pretraining. We use a dataset of chest X-ray images with bounding box labels for 13 different classes of anomalies. The networks are pretrained on a percentage of the dataset without labels and then finetuned on the rest of the dataset. We show that it is possible to achieve similar performance to a fully supervised model in terms of mean average precision and accuracy with only 60% of the labeled data. We also show that it is possible to increase the maximum performance of a fully-supervised model by adding a self-supervised pretraining step, and this effect can be observed with even a small amount of unlabeled data for pretraining.

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“…In addition, we wanted to implement object detection into our pipeline, giving users the opportunity to visually identify the location of lipohypertrophy being detected by our model. To implement object detection using a popular framework called YOLOv5 [ 25 , 26 ], the team created bounding boxes around the location of the lipohypertrophy masses on the positive training images using the annotated ultrasound images as a guide. Next, using the YOLOv5 framework, the YOLOv5m model was trained for 200 epochs with an image size of 320320 pixels (as this was what the Application Programming Interface allowed) and a batch size of 8.…”

Section: Methodsmentioning

confidence: 99%

Machine Learning Decision Support for Detecting Lipohypertrophy With Bedside Ultrasound: Proof-of-Concept Study

Bandari¹,

Beuzen²,

Habashy³

et al. 2022

JMIR Form Res

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Background The most common dermatological complication of insulin therapy is lipohypertrophy. Objective As a proof of concept, we built and tested an automated model using a convolutional neural network (CNN) to detect the presence of lipohypertrophy in ultrasound images. Methods Ultrasound images were obtained in a blinded fashion using a portable GE LOGIQ e machine with an L8-18I-D probe (5-18 MHz; GE Healthcare). The data were split into train, validation, and test splits of 70%, 15%, and 15%, respectively. Given the small size of the data set, image augmentation techniques were used to expand the size of the training set and improve the model’s generalizability. To compare the performance of the different architectures, the team considered the accuracy and recall of the models when tested on our test set. Results The DenseNet CNN architecture was found to have the highest accuracy (76%) and recall (76%) in detecting lipohypertrophy in ultrasound images compared to other CNN architectures. Additional work showed that the YOLOv5m object detection model could be used to help detect the approximate location of lipohypertrophy in ultrasound images identified as containing lipohypertrophy by the DenseNet CNN. Conclusions We were able to demonstrate the ability of machine learning approaches to automate the process of detecting and locating lipohypertrophy.

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Intelligent Solutions in Chest Abnormality Detection Based on YOLOv5 and ResNet50

Cited by 41 publications

References 51 publications

Relationship between Liquid-Based Cytology Preservative Solutions and Artificial Intelligence: Liquid-Based Cytology Specimen Cell Detection Using YOLOv5 Deep Convolutional Neural Network

Relationship between Liquid-Based Cytology Preservative Solutions and Artificial Intelligence: Liquid-Based Cytology Specimen Cell Detection Using YOLOv5 Deep Convolutional Neural Network

Self-Supervised Learning as a Means To Reduce the Need for Labeled Data in Medical Image Analysis

Machine Learning Decision Support for Detecting Lipohypertrophy With Bedside Ultrasound: Proof-of-Concept Study

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