Integration of Machine Learning and IoT for Assisting Medical Experts in Brain Tumor Diagnosis

Aastha,; Mishra, Sushruta; Mohanty, Satarupa

doi:10.1007/978-981-16-5304-9_10

Cited by 5 publications

(5 citation statements)

References 55 publications

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“…Multi-layered soil and environmental toxins include heavy metals, which are often regarded as one of the most toxicants. In the presence of heavy metals in the environment, living creatures are more likely to absorb these toxic materials and store them inside various body organs, such as the kidneys, livers, and bones (Abdollahi et al, 2019;Mishra and Mohanty, 2022). Aside from damaging many biological systems, the buildup of heavy metals has a detrimental effect on various bodily functions.…”

Section: Toxic Effects Of Heavy Metals Affect Human Lifementioning

confidence: 99%

“…Figure 4 illustrates how heavy metal exposure may induce health impacts, including ingesting, absorption through the skin, and breathing, which are all examples of penetration pathways for toxic heavy metals. Heavy metals' fatality rate on human health is rising Mishra and Mohanty, 2022). Because of their significant potential for toxicity, wide range of uses, and widespread presence, heavy metals need to be given more attention.…”

Section: Proposed Model For Detecting the Adverse Effects Of Heavy Me...mentioning

confidence: 99%

“…Images from a secure internet domain and samples from radiologists are gathered for our analysis. The images collected by radiologists are processed for DICOM translation into regular RGB images(Mishra and Mohanty, 2022). 100 tumors and 50 non-tumors MR images make up this collection.…”

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confidence: 99%

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A Novel Hybrid IOT Based Artificial Intelligence Algorithm for Toxicity Prediction In The Environment And Its Effect On Human Health

2023

Global NEST: The International Journal

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<p>Salem Algarni Mechanical Engineering Department, College of Engineering, King Khalid University, Abha 61421, Asir, Kingdom of Saudi Arabia Vineet Tirth Mechanical Engineering Department, College of Engineering, King Khalid University, Abha 61421, Asir, Kingdom of Saudi Arabia Talal Alqahtani Mechanical Engineering Department, College of Engineering, King Khalid University, Abha 61421, Asir, Kingdom of Saudi Arabia Pravin R. Kshirsagar Professor, Department of Artificial Intelligence, G.H. Raisoni College of Engineering, Nagpur, India</p>

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Section: Toxic Effects Of Heavy Metals Affect Human Lifementioning

confidence: 99%

Section: Proposed Model For Detecting the Adverse Effects Of Heavy Me...mentioning

confidence: 99%

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A Novel Hybrid IOT Based Artificial Intelligence Algorithm for Toxicity Prediction In The Environment And Its Effect On Human Health

2023

Global NEST: The International Journal

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“…Brain tumors, growths that form within the brain or tissues under the skull, can have benign or malignant characteristics. These tumors grow erratically, exerting pressure on the brain [4], potentially leading to several neurological complications. The World Health Organization (WHO) reports that in 2020, cancer was the second leading cause of death worldwide, accounting for approximately 10 million fatalities [5].…”

Section: Introductionmentioning

confidence: 99%

Diagnosis of Medical Images Using Fuzzy Convolutional Neural Networks

Mahdi,

Shujaa,

Zghair

2023

MMEP

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Brain tumors, characterized by the uncontrolled and rapid proliferation of cells, can result in fatal outcomes if not identified and treated promptly. Consequently, the development of a reliable and automated diagnostic system is of paramount importance. In this study, a Fuzzy Convolutional Neural Network (F-CNN) is employed for the efficient diagnosis of brain tumors (Glioma, Meningioma, Pituitary, and non-tumors), leveraging the computational capabilities of Google Colaboratory. The methodology comprises four stages: pre-processing, training, testing, and evaluation. The pre-processing stage entails rescaling the image, resizing, random flipping, and random rotation. The training phase involves the construction of an intelligent model, encompassing four blocks: convolution, ReLU, batch normalization, and max pooling. This is followed by flattening, a fuzzy inferences layer, and a dense layer with dropout. The model was trained using a Kaggle dataset comprising 7022 brain tumor MRI images and validated with a test set of 470 MRI images sourced from the Neurological Wholesale Hospital in Baghdad. The proposed F-CNN model achieved a high accuracy rate of 99.31% while maintaining low computational complexity and time. This work illustrates the potential of Deep Learning approaches, such as F-CNNs, in enhancing the precision and efficiency of medical imaging diagnostics.

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“…To diagnose brain tumors accurately, radiologists adopt magnetic resonance (MR) imaging methods to visualize the inner shape of a patient’s brain securely and reduce the chances of any surgery [ 3 , 4 ]. MR imaging helps radiologists provide necessary data regarding a patient soft cell tissues that assist the doctors while handling cases related to the brain tumor in the real-time diagnosis of a patient [ 5 , 6 , 7 ]. Accurate segments of MR images are required to help doctors with the correct identification of brain tumors using multifarious computer-aided medical instruments.…”

Section: Introductionmentioning

confidence: 99%

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

Sahoo

Mishra

Panda³

et al. 2023

Sensors

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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.

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Integration of Machine Learning and IoT for Assisting Medical Experts in Brain Tumor Diagnosis

Cited by 5 publications

References 55 publications

A Novel Hybrid IOT Based Artificial Intelligence Algorithm for Toxicity Prediction In The Environment And Its Effect On Human Health

A Novel Hybrid IOT Based Artificial Intelligence Algorithm for Toxicity Prediction In The Environment And Its Effect On Human Health

Diagnosis of Medical Images Using Fuzzy Convolutional Neural Networks

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

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