Computer-aided diagnosis of liver tumors on computed tomography images

Chang, Chin‐Chen; Chen, Honghao; Chang, Yu‐Cheng; Yang, Ming-Yang; Lo, Chung-Ming; Ko, Wei-Chun; Lee, Yee-Fan; Liu, Kao–Lang; Chang, Ruey-Feng

doi:10.1016/j.cmpb.2017.04.008

Cited by 96 publications

(47 citation statements)

References 28 publications

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“…Recently, machine learning has attracted attention as support for diagnostic tools in the medical field [12,13]. The SVM used in our study is classified as a data-knowledge integration artificial intelligence and has excellent pattern discriminability.…”

Section: Discussionmentioning

confidence: 99%

“…There have been remarkable advances in the artificial intelligence field recently, and the application of this technology to the medical field is expected [12,13]. However, there are few cases of its application to the endoscopic field except for the detection of dysplasia in patients with Barrett's esophagus and diagnosis via capsule endoscopy [14,15].…”

Section: Introductionmentioning

confidence: 99%

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Objective Assessment of the Utility of Chromoendoscopy with a Support Vector Machine

Ogawa

Nishikawa

Hideura

et al. 2018

J Gastrointest Canc

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Purpose The utility of chromoendoscopy for early gastric cancer (GC) was determined by machine learning using data of color differences. Methods Eighteen histopathologically confirmed early GC lesions were examined. We prepared images from white light endoscopy (WL), indigo carmine (Indigo), and acetic acid-indigo carmine chromoendoscopy (AIM). A border between cancerous and non-cancerous areas on endoscopic images was established from post-treatment pathological findings, and 2000 pixels with equivalent luminance values were randomly extracted from each image of cancerous and non-cancerous areas. Each pixel was represented as a three-dimensional vector with RGB values and defined as a sample. We evaluated the Mahalanobis distance using RGB values, indicative of color differences between cancerous and non-cancerous areas. We then conducted diagnosis test using a support vector machine (SVM) for each image. SVM was trained using the 100 training samples per class and determined which area each of 1900 test samples per class came from. Results The means of the Mahalanobis distances for WL, Indigo, and AIM were 1.52, 1.32, and 2.53, respectively and there were no significant differences in the three modalities. Diagnosability per endoscopy technique was assessed using the F1 measure. The means of F1 measures for WL, Indigo, and AIM were 0.636, 0.618, and 0.687, respectively. AIM images were better than WL and Indigo images for the diagnosis of GC. Conclusion Objective assessment by SVM found AIM to be suitable for diagnosis of early GC based on color differences.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Objective Assessment of the Utility of Chromoendoscopy with a Support Vector Machine

Ogawa

Nishikawa

Hideura

et al. 2018

J Gastrointest Canc

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“…Liver diseases such as fatty liver, liver fibrosis, liver cirrhosis and hepatocellular carcinoma are fast growing burden to public healthcare. 3 Classification of the liver diseases from CT images plays an important role in many liver related clinical applications. 2 Diagnostic imaging is frequently utilized to detect diseases.…”

Section: Introductionmentioning

confidence: 99%

“…One of the most common method and robust imaging technique for detection of liver disease is computed tomography (CT). 3 Classification of the liver diseases from CT images plays an important role in many liver related clinical applications.…”

Section: Introductionmentioning

confidence: 99%

Optimizing deep belief network parameters using grasshopper algorithm for liver disease classification

Renukadevi¹,

Karunakaran²

2019

Int J Imaging Syst Tech

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Image processing plays a vital role in many areas such as healthcare, military, scientific and business due to its wide variety of advantages and applications. Detection of computed tomography (CT) liver disease is one of the difficult tasks in the medical field. Hand crafted features and classifications are the two types of methods used in the previous approaches, to classify liver disease. But these classification results are not optimal. In this article, we propose a novel method utilizing deep belief network (DBN) with grasshopper optimization algorithm (GOA) for liver disease classification. Initially, the image quality is enhanced by preprocessing techniques and then features like texture, color and shape are extracted. The extracted features are reduced by utilizing the dimensionality reduction method like principal component analysis (PCA). Here, the DBN parameters are optimized using GOA for recognizing liver disease. The experiments are performed on the real time and open source CT image datasets which embraces normal, cyst, hepatoma, and cavernous hemangiomas, fatty liver, metastasis, cirrhosis, and tumor samples. The proposed method yields 98% accuracy, 95.82% sensitivity, 97.52% specificity, 98.53% precision, and 96.8% F-1 score in simulation process when compared with other existing techniques. K E Y W O R D S deep belief network (DBN), grasshopper optimization algorithm (GOA), liver disease classification, principal component analysis (PCA)

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“…More recently, fuzzy clustering [13][14][15][16][17][18][19] techniques have been implemented effectively to analyze cancer medical databases. Even though there are lots of benefits using fuzzy c-means algorithms, it has considerable drawbacks such as the result of clustering process deteriorates while uncertainty exists in the high-dimensional medical database [7,[20][21][22][23]. This paper attempts to provide suitable clustering techniques using fuzzy clustering methods for analyzing high-dimensional gene expression cancer database.…”

Section: Introductionmentioning

confidence: 99%

Effective Kernel-Based Fuzzy Clustering Systems in Analyzing Cancer Database

Kannan

Siva

Ramathilagam³

et al. 2018

Data-Enabled Discov. Appl.

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The greatest challenge in high-dimensional medical cancer databases is to differentiate the available subtypes due to uncertainty associated with the objects of the database. Recently, mathematical algorithm-based diagnosing system plays an increasingly important role in analyzing the high-dimensional medical cancer databases. Particularly, fuzzy clustering technique has taken a major role in clustering medical cancer databases. The fuzzy clustering techniques are not robust with high-dimensional databases which have more similar objects. Therefore, this paper tries to propose robust fuzzy clustering algorithms for effective analysis of high-dimensional medical database. This paper introduces the objective function of proposed robust fuzzy clustering techniques by incorporating Laplacian kernel-induced distance, Canberra distance, possibilistic memberships, and fuzzy memberships. The proposed methods have been implemented with high-dimensional breast cancer database containing three subclasses which are the leading causes of cancer deaths in the world. Benchmark datasets have been used to evaluate the performance of the proposed methods, and this paper has shown the effectiveness of the proposed methods through clustering accuracy.

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Computer-aided diagnosis of liver tumors on computed tomography images

Cited by 96 publications

References 28 publications

Objective Assessment of the Utility of Chromoendoscopy with a Support Vector Machine

Objective Assessment of the Utility of Chromoendoscopy with a Support Vector Machine

Optimizing deep belief network parameters using grasshopper algorithm for liver disease classification

Effective Kernel-Based Fuzzy Clustering Systems in Analyzing Cancer Database

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