CNN Based Multiclass Brain Tumor Detection Using Medical Imaging

Tiwari, Pallavi; Pant, Bhaskar; Elarabawy, Mahmoud M.; Abd‐Elnaby, Mohammed; Mohd, Noor; Dhiman, Gaurav; Sharma, Subhash

doi:10.1155/2022/1830010

Cited by 83 publications

(38 citation statements)

References 34 publications

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“…A fully connected three-dimensional conditional random area was built as part of post-processing to provide both visual and spatial partitioning of a channel’s result. Pallavitiwari et al’s [ 16 ] uncompressed feature computations and inter-data fusion, as shown by thorough excision testing on MRI datasets, demonstrated that our method can overcome the aforementioned problems. Our solution outperforms state-of-the-art approaches on publicly accessible benchmarks, indicating that it might be readily implemented into professional medical applications.…”

Section: Related Workmentioning

confidence: 77%

Grade Classification of Tumors from Brain Magnetic Resonance Images Using a Deep Learning Technique

et al. 2023

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To improve the accuracy of tumor identification, it is necessary to develop a reliable automated diagnostic method. In order to precisely categorize brain tumors, researchers developed a variety of segmentation algorithms. Segmentation of brain images is generally recognized as one of the most challenging tasks in medical image processing. In this article, a novel automated detection and classification method was proposed. The proposed approach consisted of many phases, including pre-processing MRI images, segmenting images, extracting features, and classifying images. During the pre-processing portion of an MRI scan, an adaptive filter was utilized to eliminate background noise. For feature extraction, the local-binary grey level co-occurrence matrix (LBGLCM) was used, and for image segmentation, enhanced fuzzy c-means clustering (EFCMC) was used. After extracting the scan features, we used a deep learning model to classify MRI images into two groups: glioma and normal. The classifications were created using a convolutional recurrent neural network (CRNN). The proposed technique improved brain image classification from a defined input dataset. MRI scans from the REMBRANDT dataset, which consisted of 620 testing and 2480 training sets, were used for the research. The data demonstrate that the newly proposed method outperformed its predecessors. The proposed CRNN strategy was compared against BP, U-Net, and ResNet, which are three of the most prevalent classification approaches currently being used. For brain tumor classification, the proposed system outcomes were 98.17% accuracy, 91.34% specificity, and 98.79% sensitivity.

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Section: Related Workmentioning

confidence: 77%

Grade Classification of Tumors from Brain Magnetic Resonance Images Using a Deep Learning Technique

et al. 2023

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“…Pallavi Tiwari et al in their study [17] suggested an automatic process for identifying multiclass sorting of brain tumors utilizing MRI. In this paper, they applied CNN for the classification brain tumor.…”

Section: Related Workmentioning

confidence: 99%

MRI-based Brain Tumor Image Classification Using CNN

Khan

Prova

Acharjee

2023

AJRCoS

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Though all brain tumors are not cancerous but they caused a critical disease produced by irrepressible and unusual dividing of cells. For the case of Medical diagnostics of many diseases, the health industry needs help, the current development in the arena of deep learning has assisted to detect diseases. In recent years medical image classification has gained remarkable attention. The most well-known neural network model for image classification problems is the Convolutional Neural Network (CNN). CNN is the frequently employed machine-learning algorithm that is used in Visual learning and Image Recognition research. It is considered to derive features adaptively through convolution, activation, pooling, and fully connected layers. In our paper, we present the convolutional neural network method to determine cancerous and non-cancerous brain tumors. We also used Data Augmentation and Image Processing to classify brain (Magnetic Resonance Imaging (MRI). We used two significant steps in our proposed system. First, different image processing techniques are used to preprocess the images and secondly we classify the preprocessed image using CNN. Brain tumor classification is a process of identifying and separating the cancerous and non-cancerous brain tissues and labeling them automatically. We use the famous machine learning algorithms Convolutional Neural Network which is broadly employed for image classifications. This experiment is conducted on a dataset of 2065 images. In our dataset number of training, examples are 1445, the number of validation examples is 310, and the number of testing example is 310. We also used data augmentation to raise the number of the dataset. We achieved a high testing accuracy of 94.39%. The proposed system displayed sufficient accuracy on the dataset and beat many of the noticeable present methods.

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“…CNN is the most popular structure for classification image detection [21]- [24]. In our study, we use CNN because we want to get the best accuracy and CNN has been proven to be very effective in image disease plant classification research [25] and also CNN extracts features automatically [26], [27].…”

Section: Introductionmentioning

confidence: 99%

Corn Plant Disease Classification Based on Leaf using Residual Networks-9 Architecture

Prasetyo

Desrony

Panjaitan

et al. 2023

IJECE

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Classification on corn plants is used to classify leaf of corn plants that are healthy and have diseases consisting of Northern Leaf Blight, Common Rust and Gray Leaf Spot. Convolutional Neural Network (CNN) is one of algorithms from the branch of deep learning that utilizes artificial neural networks to produce accurate results in classifying an image. In this study, ResNet-9 architecture implemented to build the best model CNN for classification corn plant diseases. After that we doing comparisons of epochs have been carried out to obtain the best model, including comparisons of epochs of 5, 25, 55, 75 and 100. After the epoch comparison, the highest accuracy value was obtained in the 100 epoch experiment so that in this study 100 epochs were used in model formation. The number of datasets used is 9145 data which is divided into two, there are training data (80%) and testing data (20%). In this study, three hyperparameter tuning experiments were carried out and the results of hyperparameter tuning experiments where num_workers is 4 and batch_size is 32. This classification obtained an accuracy rate of 99% and the model is implemented into a web interface.

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CNN Based Multiclass Brain Tumor Detection Using Medical Imaging

Cited by 83 publications

References 34 publications

Grade Classification of Tumors from Brain Magnetic Resonance Images Using a Deep Learning Technique

Grade Classification of Tumors from Brain Magnetic Resonance Images Using a Deep Learning Technique

MRI-based Brain Tumor Image Classification Using CNN

Corn Plant Disease Classification Based on Leaf using Residual Networks-9 Architecture

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