Brain Tumor Classification Using Modified AlexNet Network

Abhilasha, Kumari; Swati, Shipra; Kumar, Mukesh

doi:10.1007/978-981-19-1018-0_36

Cited by 4 publications

(3 citation statements)

References 6 publications

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“…For a 70:30 splitting ratio, we achieve 78.98% accuracy, and for an 80:20 splitting ratio, we achieve 82.87% accuracy A tiny dataset is used 11. (Abhilasha et al, 2022) Used an AlexNet-based architecture We describe an AlexNet-based architecture that can classify images of meningiomas, gliomas, and pituitary tumors with an accuracy of 96.38% on the testing dataset and that can be trained in less than an hour…”

Section: Discussionmentioning

confidence: 99%

Deep Learning Techniques for Brain Lesion Classification Using Various M.R.I. (from 2010 to 2022): Review and Challenges

Joshi,

Singh

2024

MEDIN

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Brain tumors are conditions brought on by the development of aberrant brain cells. They are classified into non-cancerous (benign) and cancerous (malignant). The morbidity and mortality of brain tumors are challenging to determine. A study in the United Kingdom disclosed that around 15 out of every 100 individuals with brain cancer could survive for ten or more years after being diagnosed. The remedial maneuvers of the brain tumors depend upon the kind of brain tumor, degree of cellular abnormality, location of Cancer in the brain, and other variables. The treatment decision needs assistance from the Deep learning algorithms using magnetic resonance imaging (M.R.I.) data to diagnose brain tumors due to the high dimensionalities of the remedial maneuvers. MRI is a scanning technique that uses strong radio waves and strong magnetic fields to generate detailed images of the body's interior. The study employed deep learning models to detect the tumor region in brain M.R.I. scans, including a Convolutional Neural Network model. The proposed processes involved dataset modification and preprocessing, detection, identification, and classification via CNN. Data mining techniques were utilized to uncover significant relationships and patterns from the data, resulting in successful early brain lesion identification and classification using deep learning approaches.

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Section: Discussionmentioning

confidence: 99%

Deep Learning Techniques for Brain Lesion Classification Using Various M.R.I. (from 2010 to 2022): Review and Challenges

Joshi,

Singh

2024

MEDIN

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“…In the above equation, the variables p and q are the arbitrary coefcients associated with the accuracy, and the efciency of the ftness function w, p determines the interclass variance that must be maximally assessed through equation (11). Te variable q determines the intra-class correlation that must be at its maximum, which is assessed using equation (10). Either of those will be the sublayers of the segmentation layer of the proposed model.…”

Section: Feature Selection For Segmentationmentioning

confidence: 99%

“…The existing state of art models that work over the segmentation procedures could only recognize the region of the tumor and they are not efficient in classifying the type of tumor and the progress of the tumor growth [ 9 ]. The other deep learning models like AlexNet [ 10 ], DenseNet [ 11 ], VGG-16 [ 12 ], Resnet-50 [ 13 ], and MobileNet [ 14 ] need tremendous training for attaining reasonable performance. There is a great demand for a model that works with minimal training data, especially to deal with novel diseases.…”

Section: Introductionmentioning

confidence: 99%

Variational Autoencoders‐BasedSelf‐Learning Model for Tumor Identification and Impact Analysis from 2‐D MRI Images

Srinivasu

Krishna²,

Ahmed

et al. 2023

Journal of Healthcare Engineering

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Over the past few years, a tremendous change has occurred in computer-aided diagnosis (CAD) technology. The evolution of numerous medical imaging techniques has enhanced the accuracy of the preliminary analysis of several diseases. Magnetic resonance imaging (MRI) is a prevalent technology extensively used in evaluating the progress of the spread of malignant tissues or abnormalities in the human body. This article aims to automate a computationally efficient mechanism that can accurately identify the tumor from MRI images and can analyze the impact of the tumor. The proposed model is robust enough to classify the tumors with minimal training data. The generative variational autoencoder models are efficient in reconstructing the images identical to the original images, which are used in adequately training the model. The proposed self-learning algorithm can learn from the insights from the autogenerated images and the original images. Incorporating long short-term memory (LSTM) is faster processing of the high dimensional imaging data, making the radiologist’s task and the practitioners more comfortable assessing the tumor’s progress. Self-learning models need comparatively less data for the training, and the models are more resource efficient than the various state-of-art models. The efficiency of the proposed model has been assessed using various benchmark metrics, and the obtained results have exhibited an accuracy of 89.7%. The analysis of the progress of tumor growth is presented in the current study. The obtained accuracy is not pleasing in the healthcare domain, yet the model is reasonably fair in dealing with a smaller size dataset by making use of an image generation mechanism. The study would outline the role of an autoencoder in self-learning models. Future technologies may include sturdy feature engineering models and optimized activation functions that would yield a better result.

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A systematic review on brain tumor detection using deep learning

Jain,

Sachdeva

2024

1ST INTERNATIONAL CONFERENCE ON RECENT ADVANCEMENTS IN COMPUTING TECHNOLOGIES &Amp; ENGINEERING

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Brain Tumor Classification Using Modified AlexNet Network

Cited by 4 publications

References 6 publications

Deep Learning Techniques for Brain Lesion Classification Using Various M.R.I. (from 2010 to 2022): Review and Challenges

Deep Learning Techniques for Brain Lesion Classification Using Various M.R.I. (from 2010 to 2022): Review and Challenges

Variational Autoencoders‐BasedSelf‐Learning Model for Tumor Identification and Impact Analysis from 2‐D MRI Images

A systematic review on brain tumor detection using deep learning

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