Automated Diagnosis of Various Gastrointestinal Lesions Using a Deep Learning–Based Classification and Retrieval Framework With a Large Endoscopic Database: Model Development and Validation

Owais, Muhammad; Arsalan, Muhammad; Mahmood, Tahir; Kang, Jin Kyu; Park, Kang Ryoung

doi:10.2196/18563

Cited by 17 publications

(7 citation statements)

References 63 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For ultrasound videos, deep learning is usually applied for classifying or detecting vital fetal organs. To capture the temporal features, LSTM is widely used for all kinds of medical diagnostic videos, including ultrasound [59]. LSTM can be fused with a pretrained CNN model or a CNN model which is trained from scratch.…”

Section: Discussionmentioning

confidence: 99%

A Review of Medical Diagnostic Video Analysis Using Deep Learning Techniques

et al. 2023

View full text Add to dashboard Cite

The automated analysis of medical diagnostic videos, such as ultrasound and endoscopy, provides significant benefits in clinical practice by improving the efficiency and accuracy of diagnosis. Deep learning techniques show remarkable success in analyzing these videos by automating tasks such as classification, detection, and segmentation. In this paper, we review the application of deep learning techniques for analyzing medical diagnostic videos, with a focus on ultrasound and endoscopy. The methodology for selecting the papers consists of two major steps. First, we selected around 350 papers based on the relevance of their titles to our topic. Second, we chose the research articles that focus on deep learning and medical diagnostic videos based on our inclusion and exclusion criteria. We found that convolutional neural networks (CNNs) and long short-term memory (LSTM) are the two most commonly used models that achieve good results in analyzing different types of medical videos. We also found various limitations and open challenges. We highlight the limitations and open challenges in this field, such as labeling and preprocessing of medical videos, class imbalance, and time complexity, as well as incorporating expert knowledge, k-shot learning, live feedback from experts, and medical history with video data. Our review can encourage collaborative research with domain experts and patients to improve the diagnosis of diseases from medical videos.

show abstract

Section: Discussionmentioning

confidence: 99%

A Review of Medical Diagnostic Video Analysis Using Deep Learning Techniques

et al. 2023

View full text Add to dashboard Cite

show abstract

“…They performed both binary (cancer/healthy) and multiclass (12 classes) classification tasks using specific, total, and mixture models to achieve an accuracy of 97.47%, 70.08%, and 94.7% for specific, mixture, and total specific models for the identification of cancer. Owais et al [ 144 ] deployed a DL-based classification framework for the diagnosis of gastrointestinal diseases from endoscopic images. They deployed two datasets that are publicly available: Kvasir dataset and Gastrolab dataset.…”

Section: Current Applications Of Deep Learning In Cancer Diagnosis Prognosis and Predictionmentioning

confidence: 99%

Deep Learning in Cancer Diagnosis and Prognosis Prediction: A Minireview on Challenges, Recent Trends, and Future Directions

Tufail

Kaabar

et al. 2021

Computational and Mathematical Methods in Medicine

View full text Add to dashboard Cite

Deep learning (DL) is a branch of machine learning and artificial intelligence that has been applied to many areas in different domains such as health care and drug design. Cancer prognosis estimates the ultimate fate of a cancer subject and provides survival estimation of the subjects. An accurate and timely diagnostic and prognostic decision will greatly benefit cancer subjects. DL has emerged as a technology of choice due to the availability of high computational resources. The main components in a standard computer-aided design (CAD) system are preprocessing, feature recognition, extraction and selection, categorization, and performance assessment. Reduction of costs associated with sequencing systems offers a myriad of opportunities for building precise models for cancer diagnosis and prognosis prediction. In this survey, we provided a summary of current works where DL has helped to determine the best models for the cancer diagnosis and prognosis prediction tasks. DL is a generic model requiring minimal data manipulations and achieves better results while working with enormous volumes of data. Aims are to scrutinize the influence of DL systems using histopathology images, present a summary of state-of-the-art DL methods, and give directions to future researchers to refine the existing methods.

show abstract

“…Owais et al. proposed a method in 2020 and achieved better results in 52471 endoscopic capsule images to achieve better polyp detection ( 21 ). Yanada used a novel deep learning automatic detection method in 2020 and produced a dataset to confirm the method’s effectiveness for polyp images, thus improving the early detection rate of intestinal tumors ( 22 ).…”

Section: Introductionmentioning

confidence: 99%

“…Nadimi et al proposed an improved AlexNet compounded with migration learning, data preprocessing, and data enhancement to detect capsule endoscopic polyps in 2020 and achieved 98.0% accuracy and 98.1% sensitivity (20). Owais et al proposed a method in 2020 and achieved better results in 52471 endoscopic capsule images to achieve better polyp detection (21). Yanada used a novel deep learning automatic detection method in 2020 and produced a dataset to confirm the method's effectiveness for polyp images, thus improving the early detection rate of intestinal tumors (22).…”

mentioning

confidence: 99%

Automatic polyp image segmentation and cancer prediction based on deep learning

Shen

2023

Front. Oncol.

View full text Add to dashboard Cite

The similar shape and texture of colonic polyps and normal mucosal tissues lead to low accuracy of medical image segmentation algorithms. To solve these problems, we proposed a polyp image segmentation algorithm based on deep learning technology, which combines a HarDNet module, attention module, and multi-scale coding module with the U-Net network as the basic framework, including two stages of coding and decoding. In the encoder stage, HarDNet68 is used as the main backbone network to extract features using four null space convolutional pooling pyramids while improving the inference speed and computational efficiency; the attention mechanism module is added to the encoding and decoding network; then the model can learn the global and local feature information of the polyp image, thus having the ability to process information in both spatial and channel dimensions, to solve the problem of information loss in the encoding stage of the network and improving the performance of the segmentation network. Through comparative analysis with other algorithms, we can find that the network of this paper has a certain degree of improvement in segmentation accuracy and operation speed, which can effectively assist physicians in removing abnormal colorectal tissues and thus reduce the probability of polyp cancer, and improve the survival rate and quality of life of patients. Also, it has good generalization ability, which can provide technical support and prevention for colon cancer.

show abstract

Automated Diagnosis of Various Gastrointestinal Lesions Using a Deep Learning–Based Classification and Retrieval Framework With a Large Endoscopic Database: Model Development and Validation

Cited by 17 publications

References 63 publications

A Review of Medical Diagnostic Video Analysis Using Deep Learning Techniques

A Review of Medical Diagnostic Video Analysis Using Deep Learning Techniques

Deep Learning in Cancer Diagnosis and Prognosis Prediction: A Minireview on Challenges, Recent Trends, and Future Directions

Automatic polyp image segmentation and cancer prediction based on deep learning

Contact Info

Product

Resources

About