Adaptive fuzzy deformable fusion and optimized CNN with ensemble classification for automated brain tumor diagnosis

Murthy, Mantripragada Yaswanth Bhanu; Anne, Koteswararao; Babu, Melingi Sunil

doi:10.1007/s13534-021-00209-5

Cited by 63 publications

(19 citation statements)

References 40 publications

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“…The major aim of the designed model is utilized for minimizing the NMSE and maximizing the spectral efficiency. NMSE is "a statistical evaluation used to compare proficiency testing results where the uncertainty in the measurement result is included," as shown in Equation (30).…”

Section: Objective Function For Hybrid Precodingmentioning

confidence: 99%

“…The gathered benchmark dataset is trained using the fully connected layer of CNN, the extracted features from CNN 28 are inputted to RNN, 29 and the channel is estimated using RNN. The maximum epoch of CNN, the learning rate of CNN and hidden neuron of CNN, 30 and the hidden neuron count of RNN are optimized using the SAS‐GSO algorithm. The main objective of the enhanced CNN‐RNN‐based channel estimation is minimizing the error rate regarding the MASE and MAE values.…”

Section: System Model and Problem Formulation Of Millimeter Wave Mass...mentioning

confidence: 99%

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Ensemble learning‐based channel estimation and hybrid precoding for millimeter‐wave massive multiple input multiple output system

Reddy

2023

Trans Emerging Tel Tech

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Millimeter‐Wave (mmWave) massive Multi‐User Multiple Input Multiple Output (MU‐MIMO) utilizes the hybrid analog precoding design helps to minimize the amount of Radio‐Frequency (RF) chains without causing loss of sum‐rate performance. The Channel Estimation (CE) is designed to demand the conditions of channels with high time‐varying features. The existing CE frameworks are extremely complex. The accurate CE is still challenging in mmWave massive MIMO system based on the hardware constraints, a maximum number of antennas, and so on. Various proficient algorithms were combined to yield reliable channel estimates. The mmWave massive MIMO system utilizes hybrid precoding to minimize energy consumption loss and reduce hardware complexity. The fully connected layer in hybrid precoding results in high energy efficiency loss. This proposed work aims to develop two ensemble deep learning models for CE and hybrid precoding of the “mmWave massive MIMO system.” Here, the estimation of the channel is performed by a “deep Convolutional Neural Network (CNN) with a Recurrent Neural Network (RNN).” With the reconstructed channel, the hybrid precoding phase is performed by deep CNN with AdaBoost. The performance of CE and hybrid precoding is improved with Self Adaptive Searched Galactic Swarm Optimization (SAS‐GSO) by attaining the maximum spectral efficiency and also minimizing the Normalized Mean‐Squared Error (NMSE). Hence, the simulation result is shown that the proposed model achieves enhanced performance. Throughout the analysis, at the 20th iteration, the SAS‐GSO‐ensemble approach gives a lower NMSE rate, which is 31%, 25.9%, 20%, 19.6%, 19.3%, 18.6%, and 18.3% superior to OMP‐Ensemble, AMP‐Ensemble, DLCS‐Ensemble, DGMP‐Ensemble, QALS‐Ensemble, DLQP‐Ensemble, and CSO‐Ensemble, respectively, for hybrid precoding. Therefore, the offered mmWave massive MIMO is achieved better performance regarding diverse error metrics.

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Section: Objective Function For Hybrid Precodingmentioning

confidence: 99%

Section: System Model and Problem Formulation Of Millimeter Wave Mass...mentioning

confidence: 99%

Ensemble learning‐based channel estimation and hybrid precoding for millimeter‐wave massive multiple input multiple output system

Reddy

2023

Trans Emerging Tel Tech

View full text Add to dashboard Cite

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“…The image enhancement and data augmentation methods are utilized to enhance the quality of the MRI scans and increase the number of training samples. In [ 27 ], the authors proposed a new brain tumor segmentation (Adaptive Fuzzy Deformable Fusion (AFDF)-based segmentation) and classification (Optimized CNN with Ensemble Classification) approach for brain tumor classification. In [ 28 ], the authors employed ML algorithms to classify the MRI scans of three freely accessible datasets and numerous pre-trained deep CNNs to obtain significant features from the MRI scans.…”

Section: Related Workmentioning

confidence: 99%

A Robust End-to-End Deep Learning-Based Approach for Effective and Reliable BTD Using MR Images

Ullah

Khan

et al. 2022

Sensors

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Detection of a brain tumor in the early stages is critical for clinical practice and survival rate. Brain tumors arise in multiple shapes, sizes, and features with various treatment options. Tumor detection manually is challenging, time-consuming, and prone to error. Magnetic resonance imaging (MRI) scans are mostly used for tumor detection due to their non-invasive properties and also avoid painful biopsy. MRI scanning of one patient’s brain generates many 3D images from multiple directions, making the manual detection of tumors very difficult, error-prone, and time-consuming. Therefore, there is a considerable need for autonomous diagnostics tools to detect brain tumors accurately. In this research, we have presented a novel TumorResnet deep learning (DL) model for brain detection, i.e., binary classification. The TumorResNet model employs 20 convolution layers with a leaky ReLU (LReLU) activation function for feature map activation to compute the most distinctive deep features. Finally, three fully connected classification layers are used to classify brain tumors MRI into normal and tumorous. The performance of the proposed TumorResNet architecture is evaluated on a standard Kaggle brain tumor MRI dataset for brain tumor detection (BTD), which contains brain tumor and normal MR images. The proposed model achieved a good accuracy of 99.33% for BTD. These experimental results, including the cross-dataset setting, validate the superiority of the TumorResNet model over the contemporary frameworks. This study offers an automated BTD method that aids in the early diagnosis of brain cancers. This procedure has a substantial impact on improving treatment options and patient survival.

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“…Murthy et al 82 proposed an automated brain tumor classification model using an optimized CNN with an ensemble of classifiers. The initial preprocessing phase is carried out by median filtering and contrast adjustment methods.…”

Section: Optimization Algorithms For Disease Identificationmentioning

confidence: 99%

A review on optimization techniques for medical image analysis

Kaur

Singh²

2022

Concurrency and Computation

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Data mining of medical imaging approaches makes it difficult to determine their value in the disease's insight, analysis, and diagnosis. Image classification presents a significant difficulty in image analysis and plays a vital part in computer-aided diagnosis. This task concerned the use of optimization techniques for the utilization of image processing, pattern recognition, and classification techniques, as well as the validation of image classification results in medical expert reports. The primary intention of this study is to analyze the performance of optimization techniques explored in the area of medical image analysis. For this motive, the optimization techniques employed in existing literature from 2012 to 2021 are reviewed in this study. The contribution of optimization-based medical image classification and segmentation utilized image modalities, data sets, and tradeoffs for each technique are also discussed in this review study. Finally, this review study provides the gap analysis of optimization techniques used in medical image analysis with the possible future research direction.

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Adaptive fuzzy deformable fusion and optimized CNN with ensemble classification for automated brain tumor diagnosis

Cited by 63 publications

References 40 publications

Ensemble learning‐based channel estimation and hybrid precoding for millimeter‐wave massive multiple input multiple output system

Ensemble learning‐based channel estimation and hybrid precoding for millimeter‐wave massive multiple input multiple output system

A Robust End-to-End Deep Learning-Based Approach for Effective and Reliable BTD Using MR Images

A review on optimization techniques for medical image analysis

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