CNN-Based Brain Tumor Detection Model Using Local Binary Pattern and Multilayered SVM Classifier

Kolla, Morarjee; Mishra, Rupesh Kumar; Huq, S. Zahoor Ul; Vijayalata, Y.; Gopalachari, M. Venu; Siddiquee, Kazy Noor-e-Alam

doi:10.1155/2022/9015778

Cited by 26 publications

(7 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The LBP method was used after improving brain tumor images with CNN and was reported to have obtained excellent results. The success rate achieved within the scope of that study was reported as 99.23% [15]. Pattanaik et al used KNN to improve brain tumor MRI images by combining the properties of GLCM, HOG, and LBP in the 2022 study.…”

Section: Related Studiesmentioning

confidence: 95%

Brain tumor magnetic resonance images enhanced by hybrid method based on deep learning paradigm

Gül

Kaya

2023

Preprint

View full text Add to dashboard Cite

The development of software engineering has given very successful results in the field of medical diagnosis in recent years. Deep learning and machine learning applications give remarkable results in the detection, monitoring, diagnosis, and treatment of possible tumoral regions with the analysis of the obtained medical images and data mining. Studies to diagnose brain tumors are essential because of the wide variety of brain tumors, the importance of the patient's survival time, and the brain tumor's aggressive nature. Brain tumors are defined as a disease with destructive and lethal features. Detection of a brain tumor is an essential process because of the difficulty in distinguishing between abnormal and normal tissues. With the right diagnosis, the patient can get excellent treatment, extending their lifespan. Despite all the research, there are still significant limitations in detecting tumor areas because of abnormal lesion distribution. It may be challenging to locate an area with very few tumor cells because areas with such small areas frequently appear healthy. Studies are becoming more common in which automated classification of early-stage brain tumors is performed using deep learning or machine learning approaches. This study proposes a hybrid deep learning model for the detection and early diagnosis of brain tumors via magnetic resonance imaging. The dataset images were subjected to Local Binary Pattern (LBP) and Long Short-Term Memory (LSTM) algorithms. The highest accuracy rate obtained in the hybrid model created is 98.66%.

show abstract

Section: Related Studiesmentioning

confidence: 95%

Brain tumor magnetic resonance images enhanced by hybrid method based on deep learning paradigm

Gül

Kaya

2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Based on this technique, the authors obtained 98% accuracy. Work in [30] suggested an intelligent design for identifying and classifying MR images using CNN, local binary patterns (LBP), and a multilayered SVM. Through these operations, they attained 99.3% accuracy.…”

Section: Related Workmentioning

confidence: 99%

A New Distinctive Methodology for the Classification of Brain MR Images Using Histogram Based Local Feature Descriptors

Sowjanya,

Reddy,

Raveena

2023

IJCDS

View full text Add to dashboard Cite

Brain tumors can develop at any brain location with uneven boundaries and shapes. Typically, they increased rapidly due to their size doubling in twenty-five days. If they were unrecognized in earlier phases, patients suffered from various medical problems, including death. Therefore, the identification of brain tumors in the earlier stages is one of the critical aspects. In addition, an effective imaging sequence also plays a vital role in tumor diagnosis. Magnetic resonance (MR) imaging is widely used among the available scanning approaches. Therefore, in this article, we develop a distinctive novel method to classify MR-based brain images. Here, initially, we improve the brightness of brain MR images using a median filter, and then we employ image data augmentation to increase the model's accuracy. Later, we obtain the region of interest (ROI) by Otsu's thresholding and morphological operations. Then, we extracted relevant local textures and shaped informative features from the ROI using Enhanced gradient local binary patterns (EGLBPs) and Modified pyramid histogram of oriented gradients (MPHOG). Finally, we perform classification using various supervised learning approaches: support vector machine (SVM), K-nearest neighbors (KNN), and ensemble learning. All these experiments are implemented on Harvard Medical School (HMS) database. Based on the simulation results, our proposed imaging system outperformed state-of-the-art methods in classification and segmentation. Hence, our suggested framework can be used as a predictive tool for diagnosing patients with brain tumors.

show abstract

“…On the other hand, you can have information in which its classification is given by multiple categories. This is very useful in different fields, for example for the detection of cervical cancer [3], other applications can be seen in the detection of brain tumors [4], to segment the client [5], [6], categorize images [7], classify to categorize audios [8] and analyze text to optimize customer sentiment [9]. Data classification techniques have great relevance in different areas, in particular there are abundant information on the technique of support vector machines related to classification [10], because there are not many software related to data classification that use the technique.…”

Section: Introductionmentioning

confidence: 99%

Design of a optimization algorithm for binary classification

Lengua

Quiroz

Cifuentes

et al. 2023

IJEECS

View full text Add to dashboard Cite

In the present work, the design of a system to classify data is carried out, using the Scrum methodology. The validation was carried out by expert judgment, having favorable results in terms of different criteria such as; integrity, ease of use, innovation, and scalability. Regarding the development of the functional elements of the system, it was obtained; he developed the architecture of the system, the database, and the prototypes, among other points considered. From the implementation of the system, the equation of a classifying plane in three-dimensional space will be obtained, as well as the number of internal iterations that the algorithm develops, the estimated execution time, and the graph of the plane. This system is based on a recently introduced symmetric cone proximal multiplier algorithm to solve separable optimization problems, this algorithm made an application for classification-related support vector machines.

show abstract

CNN-Based Brain Tumor Detection Model Using Local Binary Pattern and Multilayered SVM Classifier

Cited by 26 publications

References 26 publications

Brain tumor magnetic resonance images enhanced by hybrid method based on deep learning paradigm

Brain tumor magnetic resonance images enhanced by hybrid method based on deep learning paradigm

A New Distinctive Methodology for the Classification of Brain MR Images Using Histogram Based Local Feature Descriptors

Design of a optimization algorithm for binary classification

Contact Info

Product

Resources

About