Classification &amp; Feature extraction of Brain tumor from MRI Images using Modified ANN Approach

Singh, Harendra; Solanki, Roop Singh

doi:10.37391/ijeer.090202

Cited by 12 publications

(5 citation statements)

References 22 publications

(2 reference statements)

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“…The fifteen types of vegetable images are resized in 64*64*3 after pre-processing of the dataset. The five types of data augmentation operation and data shuffling are processed before the training of the image classifier [13]. The vegetable image classifier has three convolution layers with relu as activation function, 62,64, and 128 filters of size; 3*3,3*3,3*3, strides =1, and valid padding respectively.…”

Section: Architecture Of Multi-class Vegetable Image Classifiermentioning

confidence: 99%

Automatic Framework for Vegetable Classification using Transfer-Learning

Singh¹,

Singh

Goel³

et al. 2022

IJEER

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Globally, fresh vegetables are a crucial part of our lives and they provide most of the vitamins, minerals, and proteins, in short, every nutrition that a growing body need. They vary in colors like; red, green, and yellow but as our ancestors say that green vegetables are a must for every age. To identify the fresh vegetable that makes our body healthy and notion positive the proposed automatic multi-class vegetable classifier is used. In this paper, a framework based on a deep learning approach has been proposed for multi-class vegetable classification from scratch. The accuracy of the proposed model is further increased using the transfer-learning concept (DenseNet201). The whole process is divided into four modules; data collection and pre-processing, data splitting, CNN model training, and testing, and performance improvement using a pre-trained DenseNet201 network. Data augmentation and data shuffling are used to free from lack of data availability during the training phase of the model. The proposed framework is more efficient and can predict the type of vegetables comparatively in less computational time (2 to 3 minutes) with an ‘Accuracy’ of 98.58%, ‘Sensitivity’ of 98.23%, and ‘Specificity’ of 94.25%.

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Section: Architecture Of Multi-class Vegetable Image Classifiermentioning

confidence: 99%

Automatic Framework for Vegetable Classification using Transfer-Learning

Singh¹,

Singh

Goel³

et al. 2022

IJEER

Self Cite

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“…Techniques Used Accuracy J. Cheng et al [14] SVM and KNN 91.28% J. S. Paul et al [16] CNN 91.43% Anaraki et al [18] GA-CNN 94.20% Bahadure et al [7] BWT+SVM 95.00% Sultan et al [26] CNN 96.13% A. Kumar et al [27] GWO+M-SVM 95.23% M. Imran Sharif [28] InceptionV3 95.20%…”

Section: ░ Table 5: State Of the Artmentioning

confidence: 99%

An Optimized Transfer Learning Based Framework for Brain Tumor Classification

Arya

Agrawal²

2022

IJEER

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Brain Tumor (BT) categorization is an indispensable task for evaluating Tumors and making an appropriate treatment. Magnetic Resonance Imaging (MRI) modality is commonly used for such an errand due to its unparalleled nature of the imaging and the actuality that it doesn't rely upon ionizing radiations. The pertinence of Deep Learning (DL) in the space of imaging has cleared the way for exceptional advancements in identifying and classifying complex medical conditions, similar to a BT. Here in the presented paper, the classification of BT through DL techniques is put forward for the characterizing BTs using open dataset which categorize them into benign and malignant. The proposed framework achieves a striking precision of 96.65%. The proposed framework can be employed to assist physicians and radiologists in validating their initial screening for brain tumor classification.

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“…At the moment, most systems for automatic detection of biomarkers process, in fact, images of rather slow processes, such as, for example, well-known and used in medicine algorithms for detecting precursors of the development of benign and malignant neoplasms, skin lesions [4], medical decision support systems for radiography and magnetic -resonance tomography [5], etc. At the same time, the issues of monitoring the main characteristics of the functioning of the human cardiovascular system using the parameters of the basic rhythm of peak R waves on cardiograms have been relatively successfully resolved.…”

Section: Introductionmentioning

confidence: 99%

Sleep-Disordered Breathing: Statistical Characteristics Of Joint Recurrent Indicators In ЕЕG Activity

Selskii,

Egorov,

Ukolov

et al. 2023

Russ Open Med J

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The purpose of this study was to identify promising candidates for the role of biomarkers associated with different degrees of the apnea-hypapnea index in patients using polysomnographic recordings. Material — The study used polysomnography data recorded in 30 patients with nocturnal respiratory dysfunction in the form of obstructive sleep apnea syndrome. Methods — Analysis of polysomnographic recordings was carried out using a joint recurrent indicator, for which further statistical characteristics were assessed: average value, geometric mean, cubic mean, median, dispersion, standard deviation, the coefficient of variation, asymmetry indicator, kurtosis indicator. Results — For all polysomnographic recordings, joint recurrence diagrams were calculated to identify time points corresponding to specific sleep events in patients with high and low apnea-hypnea index. Based on the statistical characteristics of such events, possible candidates for the role of biomarkers to diagnose apnea syndrome are introduced. Conclusion — The article presents clustering parameters and the efficiency of dividing into clusters of statistical characteristics for two groups of patients - with high and low apnea-hypnea index. Characteristics have been identified that are promising candidates for the role of biomarkers associated with the apnea-hypnea index value.

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Classification & Feature extraction of Brain tumor from MRI Images using Modified ANN Approach

Cited by 12 publications

References 22 publications

Automatic Framework for Vegetable Classification using Transfer-Learning

Automatic Framework for Vegetable Classification using Transfer-Learning

An Optimized Transfer Learning Based Framework for Brain Tumor Classification

Sleep-Disordered Breathing: Statistical Characteristics Of Joint Recurrent Indicators In ЕЕG Activity

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