An efficient automatic brain tumor classification using optimized hybrid deep neural network

Shanthi, S.; Saradha, S.; Smitha, J.A.; Prasath, N.; Anandakumar, H.

doi:10.1016/j.ijin.2022.11.003

Cited by 39 publications

(19 citation statements)

References 21 publications

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“…Healthcare professionals may intervene earlier, personalize treatment regimens, and start the right actions thanks to the early diagnosis made possible by our prediction model [20]. Early identification frequently results in more efficient treatment plans, thereby improving the quality of life for PD sufferers [21,22]. Wide-ranging effects result from this model's capacity to facilitate early detection.…”

Section: Resultsmentioning

confidence: 99%

Enhancing Parkinson’s Disease Prediction Using Deep Learning-Based Convolutional Neural Networks

Ramya. R

2024

jes

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Recently significant advances in the field of medical diagnostics can be seen. Nonetheless, diagnosing Parkinson's disease (PD) remains difficult, especially in terms of timeliness and precision. The inability of present technological methods to identify this complicated neurodegenerative disease emphasizes the urgent need for more research. PD is a complicated neurological condition that is becoming more commonplace worldwide. A timely and precise PD diagnosis is critical because it directly determines the quality of patient care and the success of treatment. However, our current diagnostic approaches, which primarily rely on clinical assessments, have substantial limits in terms of sensitivity and specificity. Thus, the need for a more reliable and objective diagnosing procedure is essential. Convolutional neural networks (CNNs) are employed in this study to investigate complex spiral and wave patterns and to overcome this significant challenge. Drawings from both PD patients and their healthy counterparts were included in the large dataset that was used to train the robust CNN model, which is a key component of the research technique. The training and intensive testing of the model on this dataset confirms its capacity to distinguish between PD instances and non-PD cases. The results of this investigation demonstrate how well the CNN model can predict Parkinson's disease (PD) based on spiral and wave patterns, with a 93% classification accuracy. The high sensitivity and specificity of the model are especially impressive because they significantly lower the chances of false positives and false negatives. The discussion that follows offers a considered evaluation of these findings, emphasising the model's potential for early diagnosis, its capacity to support professional judgments, and its crucial role in enhancing patient outcomes. This study highlights the great potential of deep learning methods for disease diagnosis, especially for PD. The effective integration of the CNN model into a React application for real-time prediction and continuous monitoring of patient performance has important implications for telemedicine and remote healthcare management. This breakthrough makes Parkinson's disease treatment more approachable and proactive.

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Section: Resultsmentioning

confidence: 99%

Enhancing Parkinson’s Disease Prediction Using Deep Learning-Based Convolutional Neural Networks

Ramya. R

2024

jes

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“…The feature selection plays a main part in the classification, because it reduces the estimation time and enhance the classification performance [13]. The DL application produces an ideal solution because it extracts prominent features from the image, better than manually extracted features [14].…”

Section: Nowadays the Methods Based On Deep Learningmentioning

confidence: 99%

Global Self-Attention Module Based Convolutional Neural Network for Brain Tumor Classification

2024

IJIES

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The early analysis of brain tumors plays a significant role in enhancing treatment and patient survival rates. The accurate diagnosis is critical to creating treatment plans that extend the lifetime of affected individuals. The existing methods have limitations like less accuracy and not able to learn global features. To overcome this issue, Global Self-Attention Module based Convolutional Neural Network (GSAM based CNN) is proposed for brain tumor classification. The Figshare dataset is preprocessed through data augmentation which is utilized to enhance the data size, and the median filter is employed to eliminate noise in an input image. The preprocessed images are given to Adaptive Kernel Fuzzy C Means (AKFCM) with Otsu thresholding for segmentation process. After segmentation, the Sine Cosine Reptile Search Algorithm (SCRSA) is employed for feature selection. Then, the features are provided to GSAM based CNN for brain tumor classification. The proposed model achieves better result on Figshare dataset on the metrics of accuracy, precision, sensitivity, specificity and f1-score, with values of 99.83%, 99.65%, 99.79%, 99.78% and 99.71%, correspondingly, when compared to the existing methods like Comprehensive Learning Elephant Herding Optimization based K-Nearest Neighbor (CLEHO based KNN) and Parallel Deep CNN (PDCNN) with data augmentation.

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“…S. Shanthi [13] has introduced a highly effective brain tumor classification method employing an optimized hybrid deep neural network. The classification of the brain image dataset involves a hybrid model that combines a Convolutional Neural Network (CNN) and a Long Short-Term Memory (LSTM).…”

Section: Literature Surveymentioning

confidence: 99%

Performance Analysis of Brain Tumor Classification on MRI images using Pretrained Deep Learning Models

et al. 2024

Preprint

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To enhance patient longevity and accurately diagnose life-threatening diseases like brain tumors, the initial step of tumor classification holds immense importance. Medical imaging technologies are instrumental in identifying pathological conditions within the brain, with Magnetic Resonance Imaging (MRI) being widely preferred due to its superior image value and non-ionizing radiation properties. The integration of deep learning, a subset of artificial intelligence, has significantly propelled the advancement of brain tumor detection from MRI scans, resulting in enhanced prediction rates. Among the various deep learning algorithms, the Convolutional Neural Network (CNN) is extensively employed for brain tumor analysis and classification. In this study, we conduct a comparative performance analysis of transfer learning-based CNN models, specifically ResNet-177 and Inception-v3, for the automatic prediction of tumor cells within the brain. The pretrained models are trained and validated using a dataset consisting of 900 images and subsequently evaluated on a separate MRI brain dataset comprising 180 images. Our research primarily focuses on leveraging the ResNet-177 and Inception v3 pretrained CNN model to accurately classify the brain tumors, and the model's performance is assessed based on metrics such as accuracy, sensitivity, specificity, and F measure. The outcomes of our study demonstrate that the Inception v3 pre-trained model exhibits highly accurate results, showcasing improved accuracy rates, sensitivity, specificity, and f measure, thereby indicating its efficiency in brain tumor classification.

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An efficient automatic brain tumor classification using optimized hybrid deep neural network

Cited by 39 publications

References 21 publications

Enhancing Parkinson’s Disease Prediction Using Deep Learning-Based Convolutional Neural Networks

Enhancing Parkinson’s Disease Prediction Using Deep Learning-Based Convolutional Neural Networks

Global Self-Attention Module Based Convolutional Neural Network for Brain Tumor Classification

Performance Analysis of Brain Tumor Classification on MRI images using Pretrained Deep Learning Models

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