A Customized VGG19 Network with Concatenation of Deep and Handcrafted Features for Brain Tumor Detection

Rajinikanth, V.; Raj, Alex Noel Joseph; Krishnan, Palani Thanaraj; Naik, Ganesh R.

doi:10.3390/app10103429

Cited by 100 publications

(46 citation statements)

References 48 publications

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“…Figure 3 presents the assisted classification of using the VGG19 of lung CT images (dimension 224 × 224 × 3 pixels) using the DF using the SoftMax classifier, and then the performance of VGG19 is validated with VGG16, ResNet18, ResNet50 and AlexNet (images with dimension of 227 × 227 × 3 pixels) [ 41 , 42 , 43 , 44 , 45 , 46 ] and the performance is compared and validated. The performance of the implemented VGG19 is validated using DF, concatenated DF + HCF and well-established binary classifiers existing in the literature [ 47 , 48 , 49 , 50 ].…”

Section: Methodsmentioning

confidence: 99%

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

et al. 2021

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Pulmonary nodule is one of the lung diseases and its early diagnosis and treatment are essential to cure the patient. This paper introduces a deep learning framework to support the automated detection of lung nodules in computed tomography (CT) images. The proposed framework employs VGG-SegNet supported nodule mining and pre-trained DL-based classification to support automated lung nodule detection. The classification of lung CT images is implemented using the attained deep features, and then these features are serially concatenated with the handcrafted features, such as the Grey Level Co-Occurrence Matrix (GLCM), Local-Binary-Pattern (LBP) and Pyramid Histogram of Oriented Gradients (PHOG) to enhance the disease detection accuracy. The images used for experiments are collected from the LIDC-IDRI and Lung-PET-CT-Dx datasets. The experimental results attained show that the VGG19 architecture with concatenated deep and handcrafted features can achieve an accuracy of 97.83% with the SVM-RBF classifier.

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Section: Methodsmentioning

confidence: 99%

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

et al. 2021

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“…The earlier works [11,16,17,23] confirmed that the combination of DF and HF (DF+HF) will improve the performance of deep-learning system. In this work, the essential HF from the brain MRI slices are mined using the well known methods such as GLCM [3,10,29], Hu [3,10,30] and LBP [31,32]. The GLCM is widely adopted due to its superior performance and the essential GLCM parameters of the MRI slices are extracted from the segmented BT by VGG-UNet.…”

Section: Handcrafted-featuresmentioning

confidence: 99%

Convolutional-Neural-Network Assisted Segmentation and SVM Classification of Brain Tumor in Clinical MRI Slices

Rajinikanth

Kadry

Nam

2021

ITC

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Due to the increased disease occurrence rates in humans, the need for the Automated Disease Diagnosis (ADD) systems is also raised. Most of the ADD systems are proposed to support the doctor during the screening and decision making process. This research aims at developing a Computer Aided Disease Diagnosis (CADD) scheme to categorize the brain tumour of 2D MRI slices into Glioblastoma/Glioma class with better accuracy. The main contribution of this research work is to develop a CADD system with Convolutional-Neural-Network (CNN) supported segmentation and classification. The proposed CADD framework consist of the following phases; (i) Image collection and resizing, (ii) Automated tumour segmentation using VGG-UNet, (iv) Deep-feature extraction using VGG16 network, (v) Handcrafted feature extraction, (vi) Finest feature choice by firefly-algorithm, and (vii) Serial feature concatenation and binary classification. The merit of the executed CADD is confirmed using an investigation realized using the benchmark as well as clinically collected brain MRI slices. In this work, a binary classification with a 10-fold cross validation is implemented using well known classifiers and the results attained with the SVM-Cubic (accuracy >98%) is superior. This result confirms that the combination of CNN assisted segmentation and classification helps to achieve enhanced disease detection accuracy.

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“…Developing a model with a high accuracy is a challenging task. Recent version of CNN models [8][9][10] have hardly focused on hyper parameters whereas we do so; the collection [2] of features that are locally available to the CNN are also a critical issue; moreover bluntly increasing the dilation rate may add to the failure of feature collections due to the sparseness of the kernel, affecting small object detection [11]. High dilation rates may affect small object detection.…”

Section: Introductionmentioning

confidence: 99%

Incremental Dilations Using CNN for Brain Tumor Classification

2020

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Brain tumor classification is a challenging task in the field of medical image processing.Technology has now enabled medical doctors to have additional aid for diagnosis. We aim toclassify brain tumors using MRI images, which were collected from anonymous patients andartificial brain simulators . In this article, we carry out a comparative study between SimpleArtificial Neural Networks with dropout, Basic Convolutional Neural Networks (CNN), and DilatedConvolutional Neural Networks. The experimental results shed light on the high classificationperformance (accuracy 97%) of Dilated CNN. On the other hand, Dilated CNN suffers from thegridding phenomenon. An incremental, even number dilation rate takes advantage of the reducedcomputational overhead and also overcomes the adverse effects of gridding. Comparative analysisbetween different combinations of dilation rates for the different convolution layers, help validatethe results. The computational overhead in terms of efficiency for training the model to reach anacceptable threshold accuracy of 90% is another parameter to compare the model performance.

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A Customized VGG19 Network with Concatenation of Deep and Handcrafted Features for Brain Tumor Detection

Cited by 100 publications

References 48 publications

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

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

Convolutional-Neural-Network Assisted Segmentation and SVM Classification of Brain Tumor in Clinical MRI Slices

Incremental Dilations Using CNN for Brain Tumor Classification

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