Brain Tumor Detection and Classification Using Cycle Generative Adversarial Networks

Gupta, R. K.; Bharti, Santosh Kumar; Kunhare, Nilesk; Sahu, Yatendra; Pathik, Nikhlesh

doi:10.1007/s12539-022-00502-6

Cited by 45 publications

(23 citation statements)

References 26 publications

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“…The differentiator and classification can help the generator perform accurate and dependable generating operations [ 37 ]. The advantage of cyclic GAN is that this model is faster than CNN as the model is more realistic in operation [ 38 , 39 ]. Another benefit is that it does not require more preprocessing but suffers from time and space complexity like CNN and RNN [ 40 ].…”

Section: Related Workmentioning

confidence: 99%

Cyclic GAN Model to Classify Breast Cancer Data for Pathological Healthcare Task

Chopra

Junath

Singh

et al. 2022

BioMed Research International

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An algorithm framework based on CycleGAN and an upgraded dual-path network (DPN) is suggested to address the difficulties of uneven staining in pathological pictures and difficulty of discriminating benign from malignant cells. CycleGAN is used for color normalization in pathological pictures to tackle the problem of uneven staining. However, the resultant detection model is ineffective. By overlapping the images, the DPN uses the addition of small convolution, deconvolution, and attention mechanisms to enhance the model’s ability to classify the texture features of pathological images on the BreaKHis dataset. The parameters that are taken into consideration for measuring the accuracy of the proposed model are false-positive rate, false-negative rate, recall, precision, and F 1 score. Several experiments are carried out over the selected parameters, such as making comparisons between benign and malignant classification accuracy under different normalization methods, comparison of accuracy of image level and patient level using different CNN models, correlating the correctness of DPN68-A network with different deep learning models and other classification algorithms at all magnifications. The results thus obtained have proved that the proposed model DPN68-A network can effectively classify the benign and malignant breast cancer pathological images at various magnifications. The proposed model also is able to better assist the pathologists in diagnosing the patients by synthesizing the images of different magnifications in the clinical stage.

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Section: Related Workmentioning

confidence: 99%

Cyclic GAN Model to Classify Breast Cancer Data for Pathological Healthcare Task

Chopra

Junath

Singh

et al. 2022

BioMed Research International

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“…In addition, Uvaneshwari and Baskar 28 presented an entirely novel approach for classifying BT that combines ML and metaheuristic optimization (BTDC‐MOML). Using brain MRI, Gupta et al 29 used InceptionResNetV2 and Random Forest Tree (RFT) to identify the cancer stage, which included glioma, meningioma, and pituitary cancer, after tumor identification. Because the dataset was limited, C‐GANs, or Cyclic Generative Adversarial Networks, were utilized to make it larger.…”

Section: Related Workmentioning

confidence: 99%

Enhancing explainability in brain tumor detection: A novel DeepEBTDNet model with LIME on MRI images

Ullah,

Hassan,

Khan

et al. 2023

Int J Imaging Syst Tech

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Early detection of brain tumors is vital for improving patient survival rates, yet the manual analysis of the extensive 3D MRI images can be error‐prone and time‐consuming. This study introduces the Deep Explainable Brain Tumor Deep Network (DeepEBTDNet), a novel deep learning model for binary classification of brain MRIs as tumorous or normal. Employing sub‐image dualistic histogram equalization (DSIHE) for enhanced image quality, DeepEBTDNet utilizes 12 convolutional layers with leaky ReLU (LReLU) activation for feature extraction, followed by a fully connected classification layer. Transparency and interpretability are emphasized through the application of the Local Interpretable Model‐Agnostic Explanations (LIME) method to explain model predictions. Results demonstrate DeepEBTDNet's efficacy in brain tumor detection, even across datasets, achieving a validation accuracy of 98.96% and testing accuracy of 94.0%. This study underscores the importance of explainable AI in healthcare, facilitating precise diagnoses and transparent decision‐making for early brain tumor identification and improved patient outcomes.

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“…In the past few years, the deep learning-based solution has gained so much popularity in every sector such as medical, agriculture, and transport [38,39]. The primary requirement of all deep learning models is the huge amount of quality data during training and testing times.…”

Section: Integrated Accident Detection Systemmentioning

confidence: 99%

AI Enabled Accident Detection and Alert System Using IoT and Deep Learning for Smart Cities

et al. 2022

Self Cite

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As the number of vehicles increases, road accidents are on the rise every day. According to the World Health Organization (WHO) survey, 1.4 million people have died, and 50 million people have been injured worldwide every year. The key cause of death is the unavailability of medical care at the accident site or the high response time in the rescue operation. A cognitive agent-based collision detection smart accident alert and rescue system will help us to minimize delays in a rescue operation that could save many lives. With the growing popularity of smart cities, intelligent transportation systems (ITS) are drawing major interest in academia and business, and are considered as a means to improve road safety in smart cities. This article proposed an intelligent accident detection and rescue system which mimics the cognitive functions of the human mind using the Internet of Things (IoTs) and the Artificial Intelligence system (AI). An IoT kit is developed that detects the accident and collects all accident-related information, such as position, pressure, gravitational force, speed, etc., and sends it to the cloud. In the cloud, once the accident is detected, a deep learning (DL) model is used to validate the output of the IoT module and activate the rescue module. Once the accident is detected by the DL module, all the closest emergency services such as the hospital, police station, mechanics, etc., are notified. Ensemble transfer learning with dynamic weights is used to minimize the false detection rate. Due to the dataset’s unavailability, a personalized dataset is generated from the various videos available on the Internet. The proposed method is validated by a comparative analysis of ResNet and InceptionResnetV2. The experiment results show that InceptionResnetV2 provides a better performance compared to ResNet with training, validation, and a test accuracy of 98%, respectively. To measure the performance of the proposed approach in the real world, it is validated on the toy car.

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Brain Tumor Detection and Classification Using Cycle Generative Adversarial Networks

Cited by 45 publications

References 26 publications

Cyclic GAN Model to Classify Breast Cancer Data for Pathological Healthcare Task

Cyclic GAN Model to Classify Breast Cancer Data for Pathological Healthcare Task

Enhancing explainability in brain tumor detection: A novel DeepEBTDNet model with LIME on MRI images

AI Enabled Accident Detection and Alert System Using IoT and Deep Learning for Smart Cities

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