Deep Neural Network-Based Novel Mathematical Model for 3D Brain Tumor Segmentation

Ladkat, Ajay S.; Bangare, Sunil L.; Jagota, Vishal; Sanober, Sumaya; Beram, Shehab Mohamed; Rane, Kantilal Pitambar; Singh, Bhupesh Kumar

doi:10.1155/2022/4271711

Cited by 30 publications

(19 citation statements)

References 45 publications

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“…Subsequently, it applies an adaptive histogram equalization process using the CLAHE method. This enhances the contrast of the image, making subtle details more perceptible [32]- [34].To further refine the enhancement, the algorithm computes the local mean and standard deviation within a specified tile size. This local statistical analysis is crucial for capturing nuances within distinct regions of the image.…”

Section: Self-adaptive -Clahe For Brain Tumors Image Enhancementmentioning

confidence: 99%

NeuroInsight: A Revolutionary Self-Adaptive Framework for Precise Brain Tumor Classification in Medical ImagingUsing Adaptive Deep Learning

Arora,

Mishra

2024

Preprint

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This study presents a robust framework for the classification of brain tumors, beginning with meticulous data curation from 233 patients. The dataset comprises a diverse range of T1-weighted contrast-enhanced images, encompassing meningioma, glioma, and pituitary tumor types. Rigorous organization, pre-processing, and augmentation techniques are applied to optimize model training. The proposed self-adaptive model incorporates a cutting-edge algorithm, leveraging Adaptive Contrast Limited Histogram Equalization (CLAHE) and Self-Adaptive Spatial Attention. CLAHE enhances grayscale images by tailoring contrast to the unique characteristics of each region. The Self-Adaptive Spatial Attention, implemented through an Attention Layer, dynamically assigns weights to spatial locations, thereby enhancing sensitivity to critical brain regions. The model architecture integrates transfer learning models, including DenseNet169, DenseNet201, ResNet152, and InceptionResNetV2, contributing to its robustness. DenseNet169 serves as a feature extractor, capturing hierarchical features through pre-trained weights. Model adaptability is further enriched by components such as batch normalization, dropout, layer normalization, and an adaptive learning rate strategy, mitigating overfitting and dynamically adjusting learning rates during training. Technical details, including the use of the Adam optimizer and softmax activation function, underscore the model's optimization and multi-class classification capabilities. The proposed model, which amalgamates transfer learning and adaptive mechanisms, emerges as a powerful tool for brain tumor detection and classification in medical imaging. Its nuanced comprehension of brain tumor images, facilitated by self-adaptive attention mechanisms, positions it as a promising advancement in computer-aided diagnosis in neuroimaging. Leveraging DenseNet201 with a self-adaptive mechanism, the model surpasses previous methods, achieving an accuracy of 94.85%, precision of 95.16%, and recall of 94.60%, showcasing its potential for enhanced accuracy and generalization in the challenging realm of medical image analysis.

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Section: Self-adaptive -Clahe For Brain Tumors Image Enhancementmentioning

confidence: 99%

NeuroInsight: A Revolutionary Self-Adaptive Framework for Precise Brain Tumor Classification in Medical ImagingUsing Adaptive Deep Learning

Arora,

Mishra

2024

Preprint

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“…In [20], they use a pre-trained deep CNN model and propose a block-wise fine-tuning strategy based on transfer learning. In [21], an efficient automated segmentation mathematical model is presented over a Deep Neural Network, which uses U-Net in segmenting the images to support classification. In [22], they apply transfer learning techniques towards brain tumor classification, which trains the features with CNN, and classification is performed with SVM.…”

Section: Related Workmentioning

confidence: 99%

Real-Time Multi-Feature Approximation Model-Based Efficient Brain Tumor Classification Using Deep Learning Convolution Neural Network Model

Panyala¹,

Baskar²

2023

Computer Systems Science and Engineering

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The deep learning models are identified as having a significant impact on various problems. The same can be adapted to the problem of brain tumor classification. However, several deep learning models are presented earlier, but they need better classification accuracy. An efficient Multi-Feature Approximation Based Convolution Neural Network (CNN) model (MFA-CNN) is proposed to handle this issue. The method reads the input 3D Magnetic Resonance Imaging (MRI) images and applies Gabor filters at multiple levels. The noise-removed image has been equalized for its quality by using histogram equalization. Further, the features like white mass, grey mass, texture, and shape are extracted from the images. Extracted features are trained with deep learning Convolution Neural Network (CNN). The network has been designed with a single convolution layer towards dimensionality reduction. The texture features obtained from the brain image have been transformed into a multi-dimensional feature matrix, which has been transformed into a single-dimensional feature vector at the convolution layer. The neurons of the intermediate layer are designed to measure White Mass Texture Support (WMTS), Gray Mass Texture Support (GMTS), White Mass Covariance Support (WMCS), Gray Mass Covariance Support (GMCS), and Class Texture Adhesive Support (CTAS). In the test phase, the neurons at the intermediate layer compute the support as mentioned above values towards various classes of images. Based on that, the method adds a Multi-Variate Feature Similarity Measure (MVFSM). Based on the importance of MVFSM, the process finds the class of brain image given and produces an efficient result.

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“…The producers have picked the Neath conditions to give the chosen classifiers the best enchanter for spotting unusual and genuine visitors in MQTT associations [33][34][35]. The newest CNN version, called a capsule network-based technique by S. Pande et al, has been proposed as the optimal architecture for maintaining links between learned characteristics across the network and the authors also proposed the KNN-based strategy for retrieving medicinal leaf information [36][37][38].…”

Section: Literature Surveymentioning

confidence: 99%

Internet of Things Based Secured Data Transmission Protocol for Agriculture Application

Bangare¹,

Patil²

2023

ISI

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In this research, we proposed a protocol for information transmission in a communication system. The protocol consists of three parts: Publisher, Broker, and Subscriber. The main problem in the transmission is the reliability of the broker. It is very difficult to get the status of the broker whether it is active or not. Secondly, we also worked on the part of security.Here by employing a Watchdog timer to determine whether the broker is active or not. To make the system more secure AES algorithm is incorporated into it. To validate the suggested protocol hardware setup is built and performance is tested in various conditions. Performance tests show that our proposed system outperforms the current system when compared to the existing system. The suggested system architecture's temporal and spatial complexity are also presented in this research.

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Deep Neural Network-Based Novel Mathematical Model for 3D Brain Tumor Segmentation

Cited by 30 publications

References 45 publications

NeuroInsight: A Revolutionary Self-Adaptive Framework for Precise Brain Tumor Classification in Medical ImagingUsing Adaptive Deep Learning

NeuroInsight: A Revolutionary Self-Adaptive Framework for Precise Brain Tumor Classification in Medical ImagingUsing Adaptive Deep Learning

Real-Time Multi-Feature Approximation Model-Based Efficient Brain Tumor Classification Using Deep Learning Convolution Neural Network Model

Internet of Things Based Secured Data Transmission Protocol for Agriculture Application

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