scholarly journals Cloud-Based Lung Tumor Detection and Stage Classification Using Deep Learning Techniques

2022 ◽  
Vol 2022 ◽  
pp. 1-17
Author(s):  
Gopi Kasinathan ◽  
Selvakumar Jayakumar
Keyword(s):  
Active Contour Model ◽  
Lung Tumor ◽  
Computer Aided Diagnosis ◽  
Healthcare Sector ◽  
Experimental Result ◽  
Tumor Segmentation ◽  
Hybrid Technique ◽  
Computer Aided ◽  
Stage Classification ◽  
Aided Diagnosis

Artificial intelligence (AI), Internet of Things (IoT), and the cloud computing have recently become widely used in the healthcare sector, which aid in better decision-making for a radiologist. PET imaging or positron emission tomography is one of the most reliable approaches for a radiologist to diagnosing many cancers, including lung tumor. In this work, we proposed stage classification of lung tumor which is a more challenging task in computer-aided diagnosis. As a result, a modified computer-aided diagnosis is being considered as a way to reduce the heavy workloads and second opinion to radiologists. In this paper, we present a strategy for classifying and validating different stages of lung tumor progression, as well as a deep neural model and data collection using cloud system for categorizing phases of pulmonary illness. The proposed system presents a Cloud-based Lung Tumor Detector and Stage Classifier (Cloud-LTDSC) as a hybrid technique for PET/CT images. The proposed Cloud-LTDSC initially developed the active contour model as lung tumor segmentation, and multilayer convolutional neural network (M-CNN) for classifying different stages of lung cancer has been modelled and validated with standard benchmark images. The performance of the presented technique is evaluated using a benchmark image LIDC-IDRI dataset of 50 low doses and also utilized the lung CT DICOM images. Compared with existing techniques in the literature, our proposed method achieved good result for the performance metrics accuracy, recall, and precision evaluated. Under numerous aspects, our proposed approach produces superior outcomes on all of the applied dataset images. Furthermore, the experimental result achieves an average lung tumor stage classification accuracy of 97%-99.1% and an average of 98.6% which is significantly higher than the other existing techniques.

scholarly journals Hybrid Image Mining Methods to Classify the Abnormality in Complete Field Image Mammograms Based on Normal Regions

2011 ◽  
pp. 80-84
Author(s):  
Aswini Kumar Mohanty ◽  
P. K. Champati ◽  
Manas Rajan Senapati ◽  
Saroj Kumar Lena
Keyword(s):  
Breast Cancer ◽  
Ground Truth ◽  
Developed Countries ◽  
Classification Performance ◽  
Computer Aided Diagnosis ◽  
Common Disease ◽  
Hybrid Technique ◽  
Classification Rate ◽  
Computer Aided ◽  
Aided Diagnosis

Breast Cancer now becomes a common disease among woman in developing as well as developed countries. Many noninvasive methodologies have been used to detect breast cancer. Computer Aided diagnosis through, Mammography is a widely used as a screening tool and is the gold standard for the early detection of breast cancer. The classification of breast masses into the benign and malignant categories is an important problem in the area of computer-aided diagnosis of breast cancer. We present a new method for complete total image of mammogram analysis. A mammogram is analyzed region by region and is classified as normal or abnormal. We present a hybrid technique for extracting features that can be used to distinguish normal and abnormal regions of a mammogram. We describe our classifier technique that uses a unique re-classification method to boost the classification performance. Our proposed hybrid technique comprises decision tree followed by association rule miner shows most proficient and promising performance with high classification rate compared to many other classifiers. We have tested this technique on a set of ground-truth complete total image of mammograms and the result was quite effective.

scholarly journals Computer-Aided Diagnosis Research of a Lung Tumor Based on a Deep Convolutional Neural Network and Global Features

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Huiling Lu
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Gradient Descent ◽  
Lung Tumor ◽  
Recognition Rate ◽  
Computer Aided Diagnosis ◽  
Deep Convolutional Neural Network ◽  
Model Parameters ◽  
Computer Aided ◽  
Aided Diagnosis

Based on the better generalization ability and the feature learning ability of the deep convolutional neural network, it is very significant to use the DCNN on the computer-aided diagnosis of a lung tumor. Firstly, a deep convolutional neural network was constructed according to the fuzzy characteristics and the complexity of lung CT images. Secondly, the relation between model parameters (iterations, different resolution) and recognition rate is discussed. Thirdly, the effects of different model structures for the identification of a lung tumor were analyzed by changing convolution kernel size, feature dimension, and depth of the network. Fourthly, the different optimization methods on how to influence the DCNN performance were discussed from three aspects containing pooling methods (maximum pooling and mean pooling), activation function (sigmoid and ReLU), and training algorithm (batch gradient descent and gradient descent with momentum). Finally, the experimental results verified the feasibility of DCNN used on computer-aided diagnosis of lung tumors, and it can achieve a good recognition rate when selecting the appropriate model parameters and model structure and using the method of gradient descent with momentum.

Mammogram Classification Using Support Vector Machine

2017 ◽  
pp. 587-614
Author(s):  
Youssef Ben Youssef ◽  
Elhassane Abdelmounim ◽  
Abdelaziz Belaguid
Keyword(s):  
Support Vector Machine ◽  
Computer Aided Diagnosis ◽  
Support Vector ◽  
Tumor Segmentation ◽  
Svm Classifier ◽  
Local Descriptors ◽  
Mammographic Image ◽  
Computer Aided ◽  
Detection And Diagnosis ◽  
Aided Diagnosis

Among the objectives of artificial intelligence techniques, we find computer-aided diagnosis systems that support preventive medical check-ups and perform detection, recognition, and classification patterns. Recently these techniques are emerged in different areas particularly in medical imaging. Medical image is an important source of information, and a golden tool for the diagnosis and assessment of a pathological analysis process. In this chapter Computer-Aided Diagnosis (CAD) system is proposed in detection and diagnosis of breast cancer, it is mainly composed of the following steps: preprocessing mammographic image, segmentation of suspect region on the mammographic image using Chan Vese model, extraction of global and local descriptors and then image classification into malignant and benign mammograms using Support Vector Machine (SVM) classifier. The analysis of mammographic images proposed system with a choice of the subset of local descriptors after tumor segmentation leads to a classification of malignant and benign mammograms. System proposed achieves 92% for accuracy.

Mammogram Classification Using Support Vector Machine

2020 ◽  
pp. 894-921
Author(s):  
Youssef Ben Youssef ◽  
Elhassane Abdelmounim ◽  
Abdelaziz Belaguid
Keyword(s):  
Support Vector Machine ◽  
Computer Aided Diagnosis ◽  
Support Vector ◽  
Tumor Segmentation ◽  
Svm Classifier ◽  
Local Descriptors ◽  
Mammographic Image ◽  
Computer Aided ◽  
Detection And Diagnosis ◽  
Aided Diagnosis

Among the objectives of artificial intelligence techniques, we find computer-aided diagnosis systems that support preventive medical check-ups and perform detection, recognition, and classification patterns. Recently these techniques are emerged in different areas particularly in medical imaging. Medical image is an important source of information, and a golden tool for the diagnosis and assessment of a pathological analysis process. In this chapter Computer-Aided Diagnosis (CAD) system is proposed in detection and diagnosis of breast cancer, it is mainly composed of the following steps: preprocessing mammographic image, segmentation of suspect region on the mammographic image using Chan Vese model, extraction of global and local descriptors and then image classification into malignant and benign mammograms using Support Vector Machine (SVM) classifier. The analysis of mammographic images proposed system with a choice of the subset of local descriptors after tumor segmentation leads to a classification of malignant and benign mammograms. System proposed achieves 92% for accuracy.

Review on MRI Brain Tumor Segmentation

2014 ◽  
Vol 626 ◽  
pp. 38-43
Author(s):  
S.A. Praylin Selva Blessy ◽  
C. Helen Sulochana
Keyword(s):  
Brain Tumor ◽  
Medical Imaging ◽  
Computer Aided Diagnosis ◽  
Future Research ◽  
Tumor Segmentation ◽  
Brain Tumor Segmentation ◽  
Mri Brain ◽  
Tumor Tissues ◽  
Computer Aided ◽  
Aided Diagnosis

Accurate segmentation of brain tumor from MRI is crucial in computer aided diagnosis as well as in other medical imaging applications. Brain tumor segmentation is a challenging task due to the diverse appearance of tumor tissues. A variety of brain tumor segmentation techniques have been explored in the literature. Here, a brief review of different brain tumor segmentation techniques has been discussed with their merits and demerits. We conclude with a discussion on the trend of future research in brain tumor segmentation.

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