Abstract
Classification of brain tumor is highly significant in the medical field in real-world to improve the progress of treatments. The seriousness behind the tumors are normally graded based on the size into grade I, grade II, grade III and grade IV. This is where the process of multi-grade brain tumor classification gains attention. Thus, the article focusses on classifying the brain MRI images into four different grades by proposing a novel and a very efficient classification strategy with high accuracy. The acquired images are pre-processed with the help of an Extended Adaptive Wiener Filter (EAWF) and then segmented using the piecewise Fuzzy C- means Clustering (piFCM) technique. Then the most ideal features such as the texture, intensity and shape features that can best explain the growth of tumors are extracted using the Local Binary Pattern (LBP) and the Hybrid Local Directional Pattern with Gabor Filter (HLDP-GF) techniques. After extracting the ideal features, the Manta Ray Foraging Optimization (MRFO) method has been introduced to optimally select the most relevant features. Finally, a Hybrid Deep Neural Network with Adaptive Rain Optimizer Algorithm (HDNN- AROA) is proposed to classify the grades of brain tumors with high accuracy and efficiency. The proposed technique has been compared with the existing state-of-the-art techniques relevant to brain tumor classification in terms of accuracy, precision, recall and dice similarity coefficient to prove the overall efficiency of the system.
Highlights
Diseases associated with the brain are evolving as a big issue in modern society malignant brain tumors which seriously affect the human lives [1]
3 Tesla (3T) Magnetic Resonance Imaging (MRI) is a non-invasive method developed to create a robust in alternative for biopsy . [8,25] Without a doubt, grade estimation in brain tumor is performed by a MRI screening method that provides a large amount of information to the radiologists and neuro-surgeons
This paper presents a solution for multi-grade brain tumor classification with higher accuracy
Summary
Diseases associated with the brain are evolving as a big issue in modern society malignant brain tumors which seriously affect the human lives [1]. To reduce the mortality rate due to brain tumors and to improve the quality of life for every patients, an efficient and highly accurate classification strategy is much required. There are a vast count of techniques available to categorize the tumors identified as harmful and harmless whereas the technological side still lags in obtaining an efficient and accurate classification results based on the stages (grades) of the tumors. The chosen neural network is highly efficient in classifying the tumors based on the growth and the accuracy of it has been further improved with the hybridization of an efficient optimization algorithm that converges better improving the learning rate. The accuracy in the classification of brain MRI images based on grades has been drastically improved using the proposed Hybrid Deep Neural Network with Adaptive Rain Optimizer Algorithm (HDNN-AROA) with better convergence.
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