An Augmented Modulated Deep Learning Based Intelligent Predictive Model for Brain Tumor Detection Using GAN Ensemble

Abstract

Brain tumor detection in the initial stage is becoming an intricate task for clinicians worldwide. The diagnosis of brain tumor patients is rigorous in the later stages, which is a serious concern. Although there are related pragmatic clinical tools and multiple models based on machine learning (ML) for the effective diagnosis of patients, these models still provide less accuracy and take immense time for patient screening during the diagnosis process. Hence, there is still a need to develop a more precise model for more accurate screening of patients to detect brain tumors in the beginning stages and aid clinicians in diagnosis, making the brain tumor assessment more reliable. In this research, a performance analysis of the impact of different generative adversarial networks (GAN) on the early detection of brain tumors is presented. Based on it, a novel hybrid enhanced predictive convolution neural network (CNN) model using a hybrid GAN ensemble is proposed. Brain tumor image data is augmented using a GAN ensemble, which is fed for classification using a hybrid modulated CNN technique. The outcome is generated through a soft voting approach where the final prediction is based on the GAN, which computes the highest value for different performance metrics. This analysis demonstrated that evaluation with a progressive-growing generative adversarial network (PGGAN) architecture produced the best result. In the analysis, PGGAN outperformed others, computing the accuracy, precision, recall, F1-score, and negative predictive value (NPV) to be 98.85, 98.45%, 97.2%, 98.11%, and 98.09%, respectively. Additionally, a very low latency of 3.4 s is determined with PGGAN. The PGGAN model enhanced the overall performance of the identification of brain cell tissues in real time. Therefore, it may be inferred to suggest that brain tumor detection in patients using PGGAN augmentation with the proposed modulated CNN technique generates the optimum performance using the soft voting approach.

A Hybrid Learning-Architecture for Improved Brain Tumor Recognition

“…Some of the compared methods (e.g., refs. [11][12][13][18][19][20]26] were tested partially on the public dataset in Table 1, while others (e.g., refs. [21,29] adopted a method similar to our approach of integrating various datasets to reduce model overfitting and avoid unbalanced data class scenarios.…”

“…Table 4 shows how the proposed approach compares to other methods tested on various brain tumor MRI data regarding accuracy. Rathi and Palani [11] [17] 83.00 88.00 80.00 Kumar et al [12] [17] 95.23 --Ismael and Abdel-Qader [13] [17] 91.9 --Deepak and Ameer [18] [17] 97.12 --Swati et al [19] [17] 94.82 93.00 94.60 Gumaei et al [20] [17] 94.23 --Anaraki et al [21] [17, [42][43][44][45] 94.20 --Sahoo et al [26] [17] 98.85 --Chaki et al [28] [46] 97.5 --Arumugam et al [29] [17,46,47] 98.56 98.64 98.45…”

“…However, reduced accuracy of 91.53% was observed when performing cross-validation experiments on different datasets. A predictive CNN model using a hybrid generative adversarial network (GAN) was proposed by Sahoo et al [26]. Both GAN-augmented samples and the original augmented dataset were fed into an in-house CNN classification model.…”