Brain Tumor Detection and Classification Using Convolutional Neural Network and Deep Neural Network

Choudhury, Chirodip Lodh; Mahanty, Chandrakanta; Kumar, Raghvendra; Mishra, Brojo Kishore

doi:10.1109/iccsea49143.2020.9132874

Cited by 96 publications

(37 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…( 1) could be produced by the mother wavelet by rotating and scaling the wave vector k h;q : The 1 st and 2 nd term considered the kernel oscillations and compensation for dc values in Eq. (1). Next, the CLAHE-based contrast enhancement process is performed, and the skull stripping procedures take place.…”

Section: Proposed Btdc-moml Modelmentioning

confidence: 99%

See 1 more Smart Citation

Computer-Aided Diagnosis Model Using Machine Learning for Brain Tumor Detection and Classification

Uvaneshwari¹,

Baskar²

2023

Computer Systems Science and Engineering

View full text Add to dashboard Cite

Section: Proposed Btdc-moml Modelmentioning

confidence: 99%

“…Brain tumours (BT), one of the most terrifying diseases, are brought on by the unchecked growth of brain cells and can be considered the most complex organ in the human body. All age groups can be affected by BT [1]. Inappropriately, the root cause of many brain cancers becomes unknown.…”

Section: Introductionmentioning

confidence: 99%

Computer-Aided Diagnosis Model Using Machine Learning for Brain Tumor Detection and Classification

Uvaneshwari¹,

Baskar²

2023

Computer Systems Science and Engineering

View full text Add to dashboard Cite

“…ANNs is used for the diagnosis of different diseases in both plant and animal caused due to different factors 19 . The capacity of ANNs to analyze large amounts of data and detect patterns warrants application in the analysis of medical images, classification of tumors, and prediction of survival 20 have somehow made easy research in medical biology. It is also very useful for solving the problem of any disease which has many confusing symptoms 21 .…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Classification Of Mushrooms Using Artificial Neural Network

Gupta¹

2022

Preprint

View full text Add to dashboard Cite

The artificial neural network (ANN) has had remarkable success in pattern recognition in recent years. It stands for a new learning paradigm in artificial intelligence (AI) and machine learning and has been applied to problems ranging from speech recognition to the prediction of protein secondary structure, cancers, and gene prediction. Recent breakthrough results in image analysis and speech recognition have generated massive interest in this field. However, the mathematical and computational methodology underlying deep learning models is very challenging, especially for interdisciplinary scientists. In this manuscript, a Neural Network model is used to classify whether a given mushroom is edible or poisonous using Tensorflow in Python based on the attributes present in the dataset. The dataset includes descriptions of hypothetical samples corresponding to 23 species of gilled mushrooms in the Agaricus and Lepiota Family Mushroom, drawn from The Audubon Society Field Guide to North American Mushrooms (1981).

show abstract

“…The performance of the proposed system is compared with the other most recent computer-aided brain tumor diagnosis systems. In these systems, CNN [33][34][35][36][37], Random Forest [38], Artificial Neural Network (ANN) [39], Deep CNN (D-CNN) [40], Support Vector Machine (SVM) [41], and Faster Region- 4 gives an overview of the performance revealed by these systems. Least accuracy at 86% is shown by the Random Forest Classifier.…”

Section: 1mentioning

confidence: 99%

Computer-Aided Brain Tumor Diagnosis: Performance Evaluation of Deep Learner CNN Using Augmented Brain MRI

Naseer

Yasir

Azhar

et al. 2021

International Journal of Biomedical Imaging

View full text Add to dashboard Cite

Brain tumor is a deadly neurological disease caused by an abnormal and uncontrollable growth of cells inside the brain or skull. The mortality ratio of patients suffering from this disease is growing gradually. Analysing Magnetic Resonance Images (MRIs) manually is inadequate for efficient and accurate brain tumor diagnosis. An early diagnosis of the disease can activate a timely treatment consequently elevating the survival ratio of the patients. Modern brain imaging methodologies have augmented the detection ratio of brain tumor. In the past few years, a lot of research has been carried out for computer-aided diagnosis of human brain tumor to achieve 100% diagnosis accuracy. The focus of this research is on early diagnosis of brain tumor via Convolution Neural Network (CNN) to enhance state-of-the-art diagnosis accuracy. The proposed CNN is trained on a benchmark dataset, BR35H, containing brain tumor MRIs. The performance and sustainability of the model is evaluated on six different datasets, i.e., BMI-I, BTI, BMI-II, BTS, BMI-III, and BD-BT. To improve the performance of the model and to make it sustainable for totally unseen data, different geometric data augmentation techniques, along with statistical standardization, are employed. The proposed CNN-based CAD system for brain tumor diagnosis performs better than other systems by achieving an average accuracy of around 98.8% and a specificity of around 0.99. It also reveals 100% correct diagnosis for two brain MRI datasets, i.e., BTS and BD-BT. The performance of the proposed system is also compared with the other existing systems, and the analysis reveals that the proposed system outperforms all of them.

show abstract

Brain Tumor Detection and Classification Using Convolutional Neural Network and Deep Neural Network

Cited by 96 publications

References 11 publications

Computer-Aided Diagnosis Model Using Machine Learning for Brain Tumor Detection and Classification

Computer-Aided Diagnosis Model Using Machine Learning for Brain Tumor Detection and Classification

Classification Of Mushrooms Using Artificial Neural Network

Computer-Aided Brain Tumor Diagnosis: Performance Evaluation of Deep Learner CNN Using Augmented Brain MRI

Contact Info

Product

Resources

About