Histopathological Gastric Cancer Detection Using Transfer Learning

Yong, Ming Ping; Hum, Yan Chai; Lai, Khin Wee; Goh, Choon Hian; Yap, Wun-She; Tee, Yee Kai

doi:10.1109/icbcb57893.2023.10246524

Cited by 3 publications

(2 citation statements)

References 18 publications

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“…This model demonstrated outstanding performance by generating intricate feature maps that focus on specific information, aiding in the detection of small polyps. A study introducing the DenseNet121 model [33], used for histopathological image analysis, achieved a top accuracy of 98.68% and an AUC of 98.58%. Lastly, a study proposed the Mask R-CNN+BiFPN model [34], which combined the object detection method with endoscopic images, improved feature fusion, and enhanced early detection of gastrointestinal lesions, achieving an mAP of 93.33%.…”

Section: Results Of the Proposed Modelmentioning

confidence: 99%

YOLO-MR: Meta-Learning-Based Lesion Detection Algorithm for Resolving Data Imbalance

Lee,

Kim,

Park

et al. 2024

IEEE Access

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The early detection and precise diagnosis of gastrointestinal diseases, particularly gastric cancer, play a vital role in improving patient survival rates and treatment outcomes. However, diagnosing these conditions can be challenging when symptoms are mild or absent. Endoscopy is commonly used for diagnosis, but it requires endoscopists with a high level of specialized knowledge to accurately identify diseases. By integrating artificial intelligence (AI) with endoscopic imaging, AI can assist in diagnosis, reduce missed cases, and enable early treatment, thereby improving patient survival rates. Previous studies have mainly focused on improving disease classification and accuracy, often overlooking disproportionate and limited medical data reliability issues. In this study, we propose a solution to address the challenges posed by imbalanced and sparse medical image data by introducing model-agnostic meta-learning (MAML).To accomplish this, we employ the YOLO-MR model, which incorporates the concept of meta-recognition into the You Only Look Once (YOLO) model. Experimental results demonstrate that the mean average precision (mAP) of the conventional YOLO model is low at 41.7, indicating a significant impact of data imbalance. Traditional data augmentation methods provide a low mAP of 65.2, while the proposed YOLO-MR model achieves an impressive mAP of 96. This is significantly higher than the conventional YOLO model by 54.3, reducing accuracy disparities between different disease classes and addressing the issue of data imbalance. Furthermore, this research showcases the effectiveness of innovative techniques such as MAML and residual blocks in addressing data imbalance in medical image recognition. These findings hold substantial potential for tackling the challenges posed by limited and imbalanced medical data in the healthcare field.INDEX TERMS Endoscopy, you only look once (YOLO), meta-learning, model-agnostic meta-learning (MAML), residual block

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Section: Results Of the Proposed Modelmentioning

confidence: 99%

YOLO-MR: Meta-Learning-Based Lesion Detection Algorithm for Resolving Data Imbalance

Lee,

Kim,

Park

et al. 2024

IEEE Access

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“…Numerous studies have reported impressive classification performance using transfer learning. Instead of training models from scratch on histopathology datasets with limited data [23], [24], [25],researchers fine-tune models pre-trained on larger source datasets on their target histopathology datasets [13], [14], [26], [27], [28].…”

Section: Introductionmentioning

confidence: 99%

Histopathological Cancer Detection Using Intra-Domain Transfer Learning and Ensemble Learning

Yong,

Hum,

Lai

et al. 2024

IEEE Access

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Cancer is a leading global cause of death. Histopathology image analysis is widely recognized as the gold standard for cancer diagnosis, playing a crucial role in early detection and reducing mortality rates. However, this diagnostic task is performed manually by pathologists, leading to high human errors and variabilities due to the huge number of images to screen and tissue complexities. With the emergence of deep learning, specifically the Convolutional Neural Network (CNN), in computer-aided diagnosis, there is a growing interest from the medical community to automate the labor-intensive manual image screening. Despite its promising performance, deep learning models still encounter challenges when it comes to extracting comprehensive histopathological features for optimal results. To tackle this issue, our study introduces deep learning models based on intra-domain transfer learning and ensemble learning. We evaluated these models on public histopathology datasets, including Gastric Histopathology Sub-size Image Database (GasHisSDB), Chaoyang colorectal, and Clinical Proteomic Tumor Analysis Consortium Clear Cell Renal Cell Carcinoma (CPTAC-CCRCC). Our models achieved state-of-the-art accuracy: 99.78% on GasHisSDB, 85.69% on Chaoyang, and 99.17% on CPTAC-CCRCC. These results highlight our models' ability to extract rich features and perform well on low-resolution histopathology images. Thus, our models have the potential to assist pathologists, reduce their workload, and enhance patient survival rates.

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Histopathological Cancer Detection Using Pre-Trained Models

Balakrishnan,

Shree

2024

2024 International Conference on Smart Systems for Electrical, Electronics, Communication and Computer Engineering (ICSSEECC)

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Histopathological Gastric Cancer Detection Using Transfer Learning

Cited by 3 publications

References 18 publications

YOLO-MR: Meta-Learning-Based Lesion Detection Algorithm for Resolving Data Imbalance

YOLO-MR: Meta-Learning-Based Lesion Detection Algorithm for Resolving Data Imbalance

Histopathological Cancer Detection Using Intra-Domain Transfer Learning and Ensemble Learning

Histopathological Cancer Detection Using Pre-Trained Models

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