Detection of Lung Nodules on CT Images based on the Convolutional Neural Network with Attention Mechanism

Lai, Khai Dinh; Nguyen, Thuy Thanh; Le, Thai Hoang

doi:10.33166/aetic.2021.02.007

Cited by 14 publications

(5 citation statements)

References 13 publications

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“…Different deep learning models were evaluated in terms of validation accuracy, and on the basis of the results, further improvement was done for model customization that was chosen on the basis of validation accuracy. Further, the proposed customized pretrained EfficientNetB7 model could be evaluated in terms of Accuracy [34], Loss or Miss classification rate [35], Precision [36], Sensitivity [37], Specificity [38], Recall [39], F1-Score [40], and MIOU (mean intersection over union).…”

Section: Resultsmentioning

confidence: 99%

Improved Multiclass Brain Tumor Detection via Customized Pretrained EfficientNetB7 Model

Khushi,

Masood,

Jaffar

et al. 2023

IEEE Access

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A brain tumor considered the deadliest disease in the world. Patients with misdiagnoses and insufficient treatment have a lower chance of surviving for life. However, for diagnosing the disturbance in the brain, magnetic resonance images play a vital role, but due to the large number of images produced by MRI, it is time-consuming and difficult to diagnose a patient at an earlier stage with high accuracy. Brain tumor detection is challenging due to variations in its size, location, and similarity in health tissues. Further, early detection of brain tumors plays a vital role in enabling a wider range of treatment options, improvements in survival rate, and patients quality of life with reduced healthcare costs. That's why there is a need for an automatic, accurate system that can detect brain tumor at an earlier stage with an accurate result. So, in this research article, pretrained modals such as AlexNet, VGG16, VGG19, ResNet50, InceptionV3, DenseNet121, and all variants of the EfficientNet model were chosen because of their exceptional performance in tasks like feature extraction and image classification with the capability of detecting anomalies in images. The experiment was done on a publicly available multiclass dataset for brain tumor. Further, before passing the dataset to the model, preprocessing was applied to the dataset in terms of resizing the images to the same size; further images were cropped due to the extra boundaries around the images; noise was removed from the images using the FastNIMeans Denoising colored filter; and a data augmentation technique was applied to reduce the overfitting problem and ensure accurate fast training. On different pretrained deep learning models, the EfficientNetB7 model produced better results in terms of validation accuracy. Further pretrained EfficientNetB7 model were customized by adding a few layers and fine-tuning parameters. Our customized pretrained EfficientNetB7 (CPEB7) model was evaluated in terms of accuracy, loss, precision, sensitivity, specificity, recall, f1-score, and MIOU (mean intersection over union) and the model achieved accuracy of 98.57%, 98.97%, 98.97%, and 99.38% for no, meningioma, pituitary, and glioma tumor classes, respectively. Further, our proposed model achieved an overall accuracy of 98.97% with a 1.02% miss classification rate and MIOU of 95.73%. Further, the proposed model was also evaluated on k-fold cross-validation and achieved 99.097% accuracy on fold-5. This proposed system shows superiority over already existing methods in terms of performance evaluation parameters.INDEX TERMS Brain Tumor, MR images, FastNIMeans Denoising colored filter, EfficientNetB7.

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Section: Resultsmentioning

confidence: 99%

Improved Multiclass Brain Tumor Detection via Customized Pretrained EfficientNetB7 Model

Khushi,

Masood,

Jaffar

et al. 2023

IEEE Access

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“…Various statistical parameters such as accuracy [48], sensitivity [49], specificity [50], positive predictive value [51], negative predictive value [52], false omission rate [53], and F1 score were applied to evaluate the performance of the convolutional neural network architectures with the optimizers.…”

Section: Resultsmentioning

confidence: 99%

Performance Analysis of State-of-the-Art CNN Architectures for LUNA16

Naseer

Akram

Masood

et al. 2022

Sensors

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The convolutional neural network (CNN) has become a powerful tool in machine learning (ML) that is used to solve complex problems such as image recognition, natural language processing, and video analysis. Notably, the idea of exploring convolutional neural network architecture has gained substantial attention as well as popularity. This study focuses on the intrinsic various CNN architectures: LeNet, AlexNet, VGG16, ResNet-50, and Inception-V1, which have been scrutinized and compared with each other for the detection of lung cancer using publicly available LUNA16 datasets. Furthermore, multiple performance optimizers: root mean square propagation (RMSProp), adaptive moment estimation (Adam), and stochastic gradient descent (SGD), were applied for this comparative study. The performances of the three CNN architectures were measured for accuracy, specificity, sensitivity, positive predictive value, false omission rate, negative predictive value, and F1 score. The experimental results showed that the CNN AlexNet architecture with the SGD optimizer achieved the highest validation accuracy for CT lung cancer with an accuracy of 97.42%, misclassification rate of 2.58%, 97.58% sensitivity, 97.25% specificity, 97.58% positive predictive value, 97.25% negative predictive value, false omission rate of 2.75%, and F1 score of 97.58%. AlexNet with the SGD optimizer was the best and outperformed compared to the other state-of-the-art CNN architectures.

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“…This research, present a spatial deep learning model for FER that incorporates the aforementioned observation and focuses on the most significant element of the face using an attention mechanism [18]. We demonstrate that by employing STN, even a simple network with a few layers can achieve a very high accuracy rate.…”

Section: Introductionmentioning

confidence: 86%

“…They claim to have outperformed the competition (70.02 percent for FER-2013). Attentional mechanisms are also used in articles [7] and [18].…”

Section: Fer Using Attentional Cnnmentioning

confidence: 99%

Enhancing Feature Extraction Technique Through Spatial Deep Learning Model for Facial Emotion Detection

Khan¹,

Singh²,

Agrawal³

2023

AETiC

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Automatic facial expression analysis is a fascinating and difficult subject that has implications in a wide range of fields, including human–computer interaction and data-driven approaches. Based on face traits, a variety of techniques are employed to identify emotions. This article examines various recent explorations into automatic data-driven approaches and handcrafted approaches for recognising face emotions. These approaches offer computationally complex solutions that provide good accuracy when training and testing are conducted on the same datasets, but they perform less well on the most difficult realistic dataset, FER-2013. The article's goal is to present a robust model with lower computational complexity that can predict emotion classes more accurately than current methods and aid society in finding a realistic, all-encompassing solution for the facial expression system. A crucial step in good facial expression identification is extracting appropriate features from the face images. In this paper, we examine how well-known deep learning techniques perform when it comes to facial expression recognition and propose a convolutional neural network-based enhanced version of a spatial deep learning model for the most relevant feature extraction with less computational complexity. That gives a significant improvement on the most challenging dataset, FER-2013, which has the problems of occlusions, scale, and illumination variations, resulting in the best feature extraction and classification and maximizing the accuracy, i.e., 74.92%. It also maximizes the correct prediction of emotions at 99.47%, and 98.5% for a large number of samples on the CK+ and FERG datasets, respectively. It is capable of focusing on the major features of the face and achieving greater accuracy over previous fashions.

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Detection of Lung Nodules on CT Images based on the Convolutional Neural Network with Attention Mechanism

Cited by 14 publications

References 13 publications

Improved Multiclass Brain Tumor Detection via Customized Pretrained EfficientNetB7 Model

Improved Multiclass Brain Tumor Detection via Customized Pretrained EfficientNetB7 Model

Performance Analysis of State-of-the-Art CNN Architectures for LUNA16

Enhancing Feature Extraction Technique Through Spatial Deep Learning Model for Facial Emotion Detection

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