Computer-aided Diagnosis for Lung Cancer

Nishio, Motohiro; Nagashima, Chihiro

doi:10.1016/j.acra.2016.11.007

Cited by 35 publications

(19 citation statements)

References 30 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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“…Chang et al [ 4 ] proposed a CADx system to diagnose liver cancer using the features of tumors obtained from multiphase CT images. Nishio and Nagashima [ 5 ] developed a CADx system to differentiate between malignant and benign nodules. Yilmaz et al [ 6 ] proposed a decision support system for effective classification of dental periapical cyst and keratocystic odontogenic tumor lesions obtained via cone beam computed tomography.…”

Section: Introductionmentioning

confidence: 99%

Automatic classification of ovarian cancer types from cytological images using deep convolutional neural networks

Yan

Liu

et al. 2018

Bioscience Reports

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Ovarian cancer is one of the most common gynecologic malignancies. Accurate classification of ovarian cancer types (serous carcinoma, mucous carcinoma, endometrioid carcinoma, transparent cell carcinoma) is an essential part in the different diagnosis. Computer-aided diagnosis (CADx) can provide useful advice for pathologists to determine the diagnosis correctly. In our study, we employed a Deep Convolutional Neural Networks (DCNN) based on AlexNet to automatically classify the different types of ovarian cancers from cytological images. The DCNN consists of five convolutional layers, three max pooling layers, and two full reconnect layers. Then we trained the model by two group input data separately, one was original image data and the other one was augmented image data including image enhancement and image rotation. The testing results are obtained by the method of 10-fold cross-validation, showing that the accuracy of classification models has been improved from 72.76 to 78.20% by using augmented images as training data. The developed scheme was useful for classifying ovarian cancers from cytological images.

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“…(Rani et al, 2016) .The probabilistic outputs of the systems and surrogate ground were analyzed by using receiver operating characteristic analysis and area under the curve. (Nishio and Nagashima, 2017) . Computed Tomography (CT) is being the most sought because of imaging sensitivity, high resolution and isotropic acquisition in locating the lung lesions.…”

Section: Introductionmentioning

confidence: 99%

Patch analysis based lung cancer classification

N¹,

Rakshitha²,

Rohith³

2019

ijrps

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Lung Cancer may be a variety of Cancer that begins in the Lungs because of those that smokes often. However, there Area unit rare probabilities those area unit non-smokers get Affected because of unhealthy pollution and Harmful gasses. The detection of tumor is incredibly vital that helps to detect affected neoplasm areas in the lungs. Computed tomography help us to understand the cancer positions in patients. The detection of cancer tumours are performed by scanning the images of computed tomography. Lung cancer identification system goes with a method of Morphological opening and Gray level co-occurrence matrix (GLCM) feature extraction and Normalized cross-correlation with patches Analysis. Lung cancer classification using Linear Discriminant Analysis (LDA) gives good results of Accuracy of 81.81%. Patch Analysis is a new method to find lung cancer.

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Computer-aided Diagnosis for Lung Cancer

Cited by 35 publications

References 30 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

Automatic classification of ovarian cancer types from cytological images using deep convolutional neural networks

Patch analysis based lung cancer classification

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