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

Abstract

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.

An Optimized Hyperparameter of Convolutional Neural Network Algorithm for Bug Severity Prediction in Alzheimer’s-Based IoT System

“…e proposed system performed better with average 98% accuracy and around 0.99 specificity by comparing other systems [24]. In [25], Gaussian convolutional neural network (GCNN) was proposed on two datasets to detecting distinctive brain tumor types.…”

“…Six-different datasets used for evaluate the model performance and to enhance the performance different geometric data augmentation techniques, with statistical standardization are selected. The proposed system performed better with average 98% accuracy and around 0.99 specificity by comparing other systems [ 24 ]. In [ 25 ], Gaussian convolutional neural network (GCNN) was proposed on two datasets to detecting distinctive brain tumor types.…”

A Machine Learning and Deep Learning Approach for Recognizing Handwritten Digits

“…ey can also be quite powerful for classifying nonimage statistics, including audio, time collection, and sign information. In addition, packages that call for item recognition and pc vision-such as self-riding automobiles and face-recognition packages-depend heavily on CNN [26][27][28].…”

Effectiveness of Learning Systems from Common Image File Types to Detect Osteosarcoma Based on Convolutional Neural Networks (CNNs) Models

“…CNNs have been widely utilized to solve many issues in various fields, but their performance in image processing for health applications is outstanding. There is a lot of research that proposes CAD-based detection for the lesion of disease [16,17]. CNNs are currently the most widely used for the computer vision application using the deep learning (DL) approaches.…”