VGG19 Network Assisted Joint Segmentation and Classification of Lung Nodules in CT Images

Khan, Muhammad Attique; Rajinikanth, V.; Satapathy, Suresh Chandra; Taniar, David; Mohanty, Jnyana Ranjan; Tariq, Usman; Damaševičius, Robertas

doi:10.3390/diagnostics11122208

Cited by 84 publications

(35 citation statements)

References 55 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It was first used to detect and classify COVID-19 and viral pneumonia [ 7 ]. Deep neural network-based models have been successful in learning the discriminative features in image-based disease classification tasks such as tuberculosis detection [ 1 ] and lung disease classification [ 3 ] in radiographs [ 1 ] and lung nodule classification in CT scans [ 2 , 4 ].…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, CXR can be used as a tool for diagnosing a number of diseases and complications, such as thoracic diseases, fractures, tooth decay, infections, osteoporosis, enlarged hearts, blocked blood vessels, etc [ 2 , 4 , 7 ]. At present, the world is grappling with COVID-19, with Omicron being the most recent variant of concern.…”

Section: Introductionmentioning

confidence: 99%

“…A great deal of research has been done on the detection of COVID-19 in CXR images. Researchers have attempted to resolve the problem in a variety of ways, resulting in the development of new classifiers, datasets, preprocessing techniques, and performance metrics [ 2 – 4 , 7 ]. While deep networks have the advantage of training models from scratch, they suffer from overfitting on small datasets.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Lung Disease Classification in CXR Images Using Hybrid Inception-ResNet-v2 Model and Edge Computing

Sharma

Goyal²,

Chariar

et al. 2022

Journal of Healthcare Engineering

View full text Add to dashboard Cite

Chest X-ray (CXR) imaging is one of the most widely used and economical tests to diagnose a wide range of diseases. However, even for expert radiologists, it is a challenge to accurately diagnose diseases from CXR samples. Furthermore, there remains an acute shortage of trained radiologists worldwide. In the present study, a range of machine learning (ML), deep learning (DL), and transfer learning (TL) approaches have been evaluated to classify diseases in an openly available CXR image dataset. A combination of the synthetic minority over-sampling technique (SMOTE) and weighted class balancing is used to alleviate the effects of class imbalance. A hybrid Inception-ResNet-v2 transfer learning model coupled with data augmentation and image enhancement gives the best accuracy. The model is deployed in an edge environment using Amazon IoT Core to automate the task of disease detection in CXR images with three categories, namely pneumonia, COVID-19, and normal. Comparative analysis has been given in various metrics such as precision, recall, accuracy, AUC-ROC score, etc. The proposed technique gives an average accuracy of 98.66%. The accuracies of other TL models, namely SqueezeNet, VGG19, ResNet50, and MobileNetV2 are 97.33%, 91.66%, 90.33%, and 76.00%, respectively. Further, a DL model, trained from scratch, gives an accuracy of 92.43%. Two feature-based ML classification techniques, namely support vector machine with local binary pattern (SVM + LBP) and decision tree with histogram of oriented gradients (DT + HOG) yield an accuracy of 87.98% and 86.87%, respectively.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Lung Disease Classification in CXR Images Using Hybrid Inception-ResNet-v2 Model and Edge Computing

Sharma

Goyal²,

Chariar

et al. 2022

Journal of Healthcare Engineering

View full text Add to dashboard Cite

show abstract

“…To perform the feature extraction and classification lot of models have been proposed [ 18 ]. To extract the features and to perform mammogram images classification into malignant and benign CAD is developed by deploying the Deep Convolutional Neural Network (DCNN) and AlexNet model [ 19 ].…”

Section: Introductionmentioning

confidence: 99%

Breast Cancer Mammograms Classification Using Deep Neural Network and Entropy-Controlled Whale Optimization Algorithm

2022

View full text Add to dashboard Cite

Breast cancer has affected many women worldwide. To perform detection and classification of breast cancer many computer-aided diagnosis (CAD) systems have been established because the inspection of the mammogram images by the radiologist is a difficult and time taken task. To early diagnose the disease and provide better treatment lot of CAD systems were established. There is still a need to improve existing CAD systems by incorporating new methods and technologies in order to provide more precise results. This paper aims to investigate ways to prevent the disease as well as to provide new methods of classification in order to reduce the risk of breast cancer in women’s lives. The best feature optimization is performed to classify the results accurately. The CAD system’s accuracy improved by reducing the false-positive rates.The Modified Entropy Whale Optimization Algorithm (MEWOA) is proposed based on fusion for deep feature extraction and perform the classification. In the proposed method, the fine-tuned MobilenetV2 and Nasnet Mobile are applied for simulation. The features are extracted, and optimization is performed. The optimized features are fused and optimized by using MEWOA. Finally, by using the optimized deep features, the machine learning classifiers are applied to classify the breast cancer images. To extract the features and perform the classification, three publicly available datasets are used: INbreast, MIAS, and CBIS-DDSM. The maximum accuracy achieved in INbreast dataset is 99.7%, MIAS dataset has 99.8% and CBIS-DDSM has 93.8%. Finally, a comparison with other existing methods is performed, demonstrating that the proposed algorithm outperforms the other approaches.

show abstract

“…However, the classification of histopathological images with different degrees of differentiation has problems such as time-consuming, labor-intensive, and large manual investment. At the same time, due to the lack of experience of doctors or the fatigue caused by the doctors working for a long time and the individual's subjective consciousness are likely to cause misjudgment, which seriously affects the formulation of the patient's treatment plan and prognostic effect [ 16 , 17 ]. It has important research value for the further study of histopathological image classification of liver cancer [ 18 – 20 ].…”

Section: Introductionmentioning

confidence: 99%

Classification of multi-differentiated liver cancer pathological images based on deep learning attention mechanism

Chen

et al. 2022

BMC Med Inform Decis Mak

View full text Add to dashboard Cite

Purpose Liver cancer is one of the most common malignant tumors in the world, ranking fifth in malignant tumors. The degree of differentiation can reflect the degree of malignancy. The degree of malignancy of liver cancer can be divided into three types: poorly differentiated, moderately differentiated, and well differentiated. Diagnosis and treatment of different levels of differentiation are crucial to the survival rate and survival time of patients. As the gold standard for liver cancer diagnosis, histopathological images can accurately distinguish liver cancers of different levels of differentiation. Therefore, the study of intelligent classification of histopathological images is of great significance to patients with liver cancer. At present, the classification of histopathological images of liver cancer with different degrees of differentiation has disadvantages such as time-consuming, labor-intensive, and large manual investment. In this context, the importance of intelligent classification of histopathological images is obvious. Methods Based on the development of a complete data acquisition scheme, this paper applies the SENet deep learning model to the intelligent classification of all types of differentiated liver cancer histopathological images for the first time, and compares it with the four deep learning models of VGG16, ResNet50, ResNet_CBAM, and SKNet. The evaluation indexes adopted in this paper include confusion matrix, Precision, recall, F1 Score, etc. These evaluation indexes can be used to evaluate the model in a very comprehensive and accurate way. Results Five different deep learning classification models are applied to collect the data set and evaluate model. The experimental results show that the SENet model has achieved the best classification effect with an accuracy of 95.27%. The model also has good reliability and generalization ability. The experiment proves that the SENet deep learning model has a good application prospect in the intelligent classification of histopathological images. Conclusions This study also proves that deep learning has great application value in solving the time-consuming and laborious problems existing in traditional manual film reading, and it has certain practical significance for the intelligent classification research of other cancer histopathological images.

show abstract

VGG19 Network Assisted Joint Segmentation and Classification of Lung Nodules in CT Images

Cited by 84 publications

References 55 publications

Lung Disease Classification in CXR Images Using Hybrid Inception-ResNet-v2 Model and Edge Computing

Lung Disease Classification in CXR Images Using Hybrid Inception-ResNet-v2 Model and Edge Computing

Breast Cancer Mammograms Classification Using Deep Neural Network and Entropy-Controlled Whale Optimization Algorithm

Classification of multi-differentiated liver cancer pathological images based on deep learning attention mechanism

Contact Info

Product

Resources

About