YOLO and residual network for colorectal cancer cell detection and counting

Haq, Inayatul; Mazhar, Tehseen; Asif, Rizwana Naz; Ghadi, Yazeed Yasin; Ullah, Najib; Khan, Muhammad Amir; Al-Rasheed, Amal

doi:10.1016/j.heliyon.2024.e24403

Heliyon

2024

DOI: 10.1016/j.heliyon.2024.e24403

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YOLO and residual network for colorectal cancer cell detection and counting

Inayatul Haq,

Tehseen Mazhar,

Rizwana Naz Asif

et al.

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Cited by 10 publications

References 49 publications

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Quantification of Empty Lacunae in Tissue Sections of Osteonecrosis of the Femoral Head Using YOLOv8 Artificial Intelligence Model

Shinohara,

Inui,

Murayama

et al. 2024

J Biomed Mater Res

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Histomorphometry is an important technique in the evaluation of non‐traumatic osteonecrosis of the femoral head (ONFH). Quantification of empty lacunae and pyknotic cells on histological images is the most reliable measure of ONFH pathology, yet it is time and manpower consuming. This study focused on the application of artificial intelligence (AI) technology to tissue image evaluation. The aim of this study is to establish an automated cell counting platform using YOLOv8 as an object detection model on ONFH tissue images and to evaluate and validate its accuracy. From 30 ONFH model rabbits, 270 tissue images were prepared; based on evaluations by three researchers, ground truth labels were created to classify each cell in the image into two classes (osteocytes and empty lacunae) or three classes (osteocytes, pyknotic cells, and empty lacunae). Two and three classes were then annotated on each image. Transfer learning based on annotated data (80% for training and 20% for validation) was performed using YOLOv8n and YOLOv8x with different parameters. To evaluate the detection accuracy of the training model, the mean average precision (mAP (50)) and precision‐recall curve were identified. In addition, the reliability of cell counting by YOLOv8 relative to manual cell counting was evaluated by linear regression analysis using five histological images unused in previous experiments. The mAP (50) for the detection of empty lacunae was 0.868 for the YOLOv8n and 0.883 for the YOLOv8x. The mAP (50) for the three classes was 0.735 for the YOLOv8n model and 0.750 for the YOLOv8x model. The quantification of empty lacunae by automated cell counting obtained in the learning was highly correlated with the manual counting data. The development of an AI‐applied automated cell counting platform will significantly reduce the time and effort of manual cell counting in histological analysis.

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Quantification of Empty Lacunae in Tissue Sections of Osteonecrosis of the Femoral Head Using YOLOv8 Artificial Intelligence Model

Shinohara,

Inui,

Murayama

et al. 2024

J Biomed Mater Res

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Leveraging AI models for lesion detection in osteonecrosis of the femoral head and T1‐weighted MRI generation from radiographs

Shinohara,

Inui,

Hwang

et al. 2024

Journal Orthopaedic Research

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This study emphasizes the importance of early detection of osteonecrosis of the femoral head (ONFH) in young patients on long‐term glucocorticoid therapy, including those with acute lymphoblastic leukemia, lupus, and other diagnoses. While X‐ray and magnetic resonance imaging (MRI) are standard imaging methods for staging ONFH, MRI can be costly and time‐consuming. The research focuses on utilizing artificial intelligence (AI) to enhance the evaluation of radiographic images for ONFH detection. The study involved analyzing X‐ray and MRI from 102 control hips and 104 ONFH‐affected hips at Association Research Circulation Osseous (ARCO) Stage II and IIIa. We employed transfer learning with the YOLOv8 model for object detection, using 80% of the data for training and 20% for validation, then assessed detection accuracy through mean average precision (mAP) and a precision‐recall curve. Additionally, AI generated synthetic MRI (sMRI) from X‐ray images using a Generative Adversarial Network (GAN) and evaluated their similarity to original MRI. Results showed that the mAP for ONFH detection was 0.923 for the YOLOv8n model and 0.951 for YOLOv8x. The GAN‐generated sMRI exhibited lower image quality compared with originals but maintained potential for lesion assessment. Intrarater reliability among evaluators was high. The findings indicate that AI techniques, particularly YOLOv8 for object detection and GAN for image generation, can effectively assist in ONFH screening, despite some limitations in the generated MRI quality.

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Integrating YOLO and WordNet for automated image object summarization

Saqib,

Aftab,

Mazhar

et al. 2024

SIViP

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YOLO and residual network for colorectal cancer cell detection and counting

Cited by 10 publications

References 49 publications

Quantification of Empty Lacunae in Tissue Sections of Osteonecrosis of the Femoral Head Using YOLOv8 Artificial Intelligence Model

Quantification of Empty Lacunae in Tissue Sections of Osteonecrosis of the Femoral Head Using YOLOv8 Artificial Intelligence Model

Leveraging AI models for lesion detection in osteonecrosis of the femoral head and T1‐weighted MRI generation from radiographs

Integrating YOLO and WordNet for automated image object summarization

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