Hybrid deep neural network with adaptive rain optimizer algorithm for multi-grade brain tumor classification of MRI images

Sasank, V. V. S.; Venkateswarlu, S.

doi:10.1007/s11042-022-12106-9

Cited by 15 publications

(4 citation statements)

References 32 publications

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“…The MRI images were examined using the LBP algorithm and the Hybrid Local Directional Pattern with Gabor Filter (HLDP-GF) algorithm within the scope of the study conducted by researchers Sasank and Venkateswarlu. In the research, a very impressive results rate of 99.5% was obtained [18]. Mohammed et al conducted another study in 2023, and the researchers achieved a striking result of 99.9% with the hybrid study based on the LBP algorithm [19].…”

Section: Related Studiesmentioning

confidence: 93%

Brain tumor magnetic resonance images enhanced by hybrid method based on deep learning paradigm

Gül

Kaya

2023

Preprint

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The development of software engineering has given very successful results in the field of medical diagnosis in recent years. Deep learning and machine learning applications give remarkable results in the detection, monitoring, diagnosis, and treatment of possible tumoral regions with the analysis of the obtained medical images and data mining. Studies to diagnose brain tumors are essential because of the wide variety of brain tumors, the importance of the patient's survival time, and the brain tumor's aggressive nature. Brain tumors are defined as a disease with destructive and lethal features. Detection of a brain tumor is an essential process because of the difficulty in distinguishing between abnormal and normal tissues. With the right diagnosis, the patient can get excellent treatment, extending their lifespan. Despite all the research, there are still significant limitations in detecting tumor areas because of abnormal lesion distribution. It may be challenging to locate an area with very few tumor cells because areas with such small areas frequently appear healthy. Studies are becoming more common in which automated classification of early-stage brain tumors is performed using deep learning or machine learning approaches. This study proposes a hybrid deep learning model for the detection and early diagnosis of brain tumors via magnetic resonance imaging. The dataset images were subjected to Local Binary Pattern (LBP) and Long Short-Term Memory (LSTM) algorithms. The highest accuracy rate obtained in the hybrid model created is 98.66%.

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Section: Related Studiesmentioning

confidence: 93%

Brain tumor magnetic resonance images enhanced by hybrid method based on deep learning paradigm

Gül

Kaya

2023

Preprint

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“…Sasank and Venkateswarlu [109] used MRFO to choose the most crucial characteristics for classifying brain tumors.…”

Section: B Feature Selectionmentioning

confidence: 99%

Manta Ray Foraging Optimization Algorithm: Modifications and Applications

et al. 2023

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The novel metaheuristic manta ray foraging optimization (MRFO) algorithm is based on the smart conduct of manta rays. The MRFO algorithm is a newly developed swarm-based metaheuristic approach that emulates the supportive conduct performed by manta rays in search of food. The MRFO algorithm efficiently resolves several optimization difficulties in various domains due to its ability to provide an equilibrium between global and local searches during the search procedure, resulting in nearly optimal results. Thus, researchers have developed several variants of MRFO since its introduction. This paper provides an in-depth examination of recent MRFO research. First, the paper introduces the natural inspiration context of MRFO and its conceptual optimization framework, and then MRFO modifications, hybridizations, and applications across different domains are discussed. Finally, a meta-analysis of the developments of the MRFO is presented along with the possible future research directions. This study can be useful for researchers and practitioners in optimization, engineering design, machine learning, scheduling, image processing, and other fields.

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“…Optimized CNN achieves the final classification phase with three ensemble classifiers: autoencoder, deep neural network (DNN), and support vector machine (SVM). Sasank and Venkateswarlu 83 introduced a multigrade brain tumor classification model based on the hybrid version of a deep neural network and adaptive rain optimizer algorithm. Especially, manta‐ray foraging optimization (MRFO) technique has been employed to choose the most optimal subset of features from the initial set of extracted features.…”

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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Hybrid deep neural network with adaptive rain optimizer algorithm for multi-grade brain tumor classification of MRI images

Cited by 15 publications

References 32 publications

Brain tumor magnetic resonance images enhanced by hybrid method based on deep learning paradigm

Brain tumor magnetic resonance images enhanced by hybrid method based on deep learning paradigm

Manta Ray Foraging Optimization Algorithm: Modifications and Applications

A review on optimization techniques for medical image analysis

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