Automatic Detection of Early Gastrointestinal Cancer Lesions Based on Optimal Feature Extraction from Gastroscopic Images

Liu, D.Y.; Gan, Tao; Rao, Nini; Xu, Guozhen; Zeng, Bing; Li, H.L.

doi:10.1166/jmihi.2015.1390

Cited by 8 publications

(4 citation statements)

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“…Extensive experimental results confirm that the proposed method can produce promising segmentation maps. Usually, GI images’ appearance differs across generating equipment, with an effect on GI lesion [ 31 ]. However, the proposed GAN-based approach achieves more promising results than the state-of-the-art methods, indicating the proposed method’s effectiveness on four datasets.…”

Section: Resultsmentioning

confidence: 99%

Semi-Supervised Segmentation Framework for Gastrointestinal Lesion Diagnosis in Endoscopic Images

Lonseko

Adjei

et al. 2023

JPM

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Background: Accurate gastrointestinal (GI) lesion segmentation is crucial in diagnosing digestive tract diseases. An automatic lesion segmentation in endoscopic images is vital to relieving physicians’ burden and improving the survival rate of patients. However, pixel-wise annotations are highly intensive, especially in clinical settings, while numerous unlabeled image datasets could be available, although the significant results of deep learning approaches in several tasks heavily depend on large labeled datasets. Limited labeled data also hinder trained models’ generalizability under fully supervised learning for computer-aided diagnosis (CAD) systems. Methods: This work proposes a generative adversarial learning-based semi-supervised segmentation framework for GI lesion diagnosis in endoscopic images to tackle the challenge of limited annotations. The proposed approach leverages limited annotated and large unlabeled datasets in the training networks. We extensively tested the proposed method on 4880 endoscopic images. Results: Compared with current related works, the proposed method validates better results (Dice similarity coefficient = 89.42 ± 3.92, Intersection over union = 80.04 ± 5.75, Precision = 91.72 ± 4.05, Recall = 90.11 ± 5.64, and Hausdorff distance = 23.28 ± 14.36) on the challenging multi-sited datasets, confirming the effectiveness of the proposed framework. Conclusion: We explore a semi-supervised lesion segmentation method to employ the full use of multiple unlabeled endoscopic images to improve lesion segmentation accuracy. Experimental results confirmed the potential of our method and outperformed promising results compared with the current related works. The proposed CAD system can minimize diagnostic errors.

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Section: Resultsmentioning

confidence: 99%

Semi-Supervised Segmentation Framework for Gastrointestinal Lesion Diagnosis in Endoscopic Images

Lonseko

Adjei

et al. 2023

JPM

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“…to detect various kind of cancers and tumors. Some automatic detection systems (Rahmawaty et al 2016;Solmaz and Tareripour 2016;Bakheey 2017;Khan et al 2019) classify medical images based on the features extracted using traditional handcrafted techniques whereas, others (Liu et al 2015;Li et al 2019;Bisla et al 2019;Cao et al 2019) use DLM to perform feature extraction and classification of medical images. Recently, it has been observed that researchers have proposed automatic detection systems (Yadav et al 2018;Hasan et al 2019;Almaraz-Damian 2020;Shankar and Perumal 2020) that use an amalgamation of both the techniques and these systems exhibit good performance as compared to their counter-parts.…”

Section: Related Workmentioning

confidence: 99%

Automatic Detection of Osteosarcoma Based on Integrated Features and Feature Selection Using Binary Arithmetic Optimization Algorithm

Bansal

Gehlot

Singhal

2021

Preprint

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Osteosarcoma is one of the most common malignant bone tumor mostly found in children and teenagers. Manual detection of osteosarcoma requires expertise and is a labour-intensive process. If detected on time, the mortality rate can be reduced. With the advent of new technologies, automatic detection systems are used to analyse and classify images obtained from different sources. Here, we propose an automatic detection system Integrated Features-Feature Selection Model for Classification (IF-FSM-C) that detect osteosarcoma from the high-resolution whole slide images (WSIs). The novelty of the proposed approach is the use of integrated features obtained by fusion of features extracted using traditional handcrafted feature extraction techniques and deep learning models. It is quite possible that the integrated features may contain some redundant and irrelevant features which may unnecessarily increases the computation time and leads to wastage of resources. To avoid this, we perform feature selection (FS) before giving the integrated features to the classifier. To perform feature selection, we propose two binary variants of recently proposed Arithmetic Optimization Algorithm (AOA) known as BAOA-S and BAOA-V. The selected features are given to a classifier that classifies the WSIs into Viable tumor (VT), Non-viable tumor (NVT) and non-tumor (NT). Experiments are performed and the results prove the superiority of the proposed IF-FSM-C that uses integrated features and feature selection in classifying WSIs as compared to the classifiers which use handcrafted or deep learning features alone as well as state-of-the-art methods for osteosarcoma detection.

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“…For complete set of references, readers are referred to the following list: [58], [59], [139]-[190] 9) Endoscopic Navigation (NAV) and 6-DOF Localization (LOC): Navigation refers to, using the current endoscopic image information, for determining where to go next.…”

Section: ) Endoscopic Abnormality Detection and Classificationmentioning

confidence: 99%

Survey of Computer Vision and Machine Learning in Gastrointestinal Endoscopy

Vemuri

2019

Preprint

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Automatic Detection of Early Gastrointestinal Cancer Lesions Based on Optimal Feature Extraction from Gastroscopic Images

Cited by 8 publications

References 0 publications

Semi-Supervised Segmentation Framework for Gastrointestinal Lesion Diagnosis in Endoscopic Images

Semi-Supervised Segmentation Framework for Gastrointestinal Lesion Diagnosis in Endoscopic Images

Automatic Detection of Osteosarcoma Based on Integrated Features and Feature Selection Using Binary Arithmetic Optimization Algorithm

Survey of Computer Vision and Machine Learning in Gastrointestinal Endoscopy

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