Intelligent immune clonal optimization algorithm for pulmonary nodule classification

Mao, Qi; Zhao, Shenghui; Ren, Lei; Li, Zhiwei; Tong, Dongbing; Xing, Yuan; Li, Haibo

doi:10.3934/mbe.2021208

Cited by 13 publications

(3 citation statements)

References 36 publications

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“…This section demonstrates the comparison of state-of-theart different CNN architecture, LeNet, AlexNet, VGG16, VGG19 and ResNet50 with three different optimiser Adam, SGD and RMSprop for the detection of Brain Tumour. The performance of CNN models was evaluated in terms of accuracy 33,34 with other various statistical parameters, such as miss-classification rate, sensitivity, 35 specificity, 36 negative predictive value (NPV), positive predictive value (PPV), F1-score 37,38 and false omission rate (FOR). 39 The model was implemented in Python 3.8 using the Keras tool, with a learning rate of 0.001 for each optimiser, a batch size of 16 and a value of 30 epochs.…”

Section: Resultsmentioning

confidence: 99%

Performance analysis of state‐of‐the‐art CNN architectures for brain tumour detection

Khushi,

Masood,

Jaffar

et al. 2023

Int J Imaging Syst Tech

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Deep learning models, such as convolutional neural network (CNN), are popular now a day to solve various complex problems in medical and other fields, such as image classification, object detection, recommendation of images, processing of natural languages and video and image analysis. So, the idea of studying the architecture of CNNs has gotten a lot of attention and become popular. This study analysed and contrasted the performance of many different CNN models trained on the publicly accessible Br35h dataset for the detection of brain tumours. These models included the LeNet, AlexNet, VGG16, VGG19 and ResNet50. Several optimisers were used in this research to fine‐tune the performance of the CNN model. These included Adam (adaptive moment estimation), SGD (stochastic gradient descent) and RMSprop (root‐mean‐square propagation). Accuracy, miss‐classification rate, sensitivity, specificity, NPV (negative predictive value), PPV (positive predictive value), F1‐score and false omission rate (FOR) were used to assess the efficacy of five different CNN architectures trained using three different optimisers. The experimental results showed that AlexNet architecture with SGD optimiser performed better than other CNN architecture with different optimisers and achieved the highest accuracy of 98.79% with a miss classification rate of 1.20%. It also achieved 98.98% sensitivity, 98.58% specificity, 98.93% NPV, 98.65% PPV, 98.82% F1‐score and 1.06% FOR.

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Section: Resultsmentioning

confidence: 99%

Performance analysis of state‐of‐the‐art CNN architectures for brain tumour detection

Khushi,

Masood,

Jaffar

et al. 2023

Int J Imaging Syst Tech

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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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“…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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Intelligent immune clonal optimization algorithm for pulmonary nodule classification

Cited by 13 publications

References 36 publications

Performance analysis of state‐of‐the‐art CNN architectures for brain tumour detection

Performance analysis of state‐of‐the‐art CNN architectures for brain tumour detection

Improved Multiclass Brain Tumor Detection via Customized Pretrained EfficientNetB7 Model

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

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