International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 11 Issue: 06 | Jun 2024
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Unveiling the Progress in Brain Tumor Detection: A Comprehensive Review Abhinav Nirwan1, Vinod Kumar2, Aditya Kumar3 1 M.Tech(Computer Science & Engineering) II Year, H.R.Institute of Technology, Ghaziabad, India 2Electronics & Communication Engineering, H.R.Institute of Technology , Ghaziabad, India
3Computer Science & Engineering, H.R.Institute of Technology, Ghaziabad, India ---------------------------------------------------------------------------***---------------------------------------------------------------------------
Abstract- Brain tumors remain a menacing disease calling for early detection to get a treatment that will work out most in your case. This
article dwells on a topic of brain diagnostics in the period of 2020-2024 from the view of technology novelty, machine learning applications, and emerging technologies. It speaks of the usage of techniques such as MRI, CT, PET, DTI and fMRI in health movies, cancer behavior and therapy planning. This paper will do so using machine learning techniques which include CNNs, SVMs, and random forests to support cancer diagnosis, classification and prediction. The discussed advantage of possible modern technologies like nanotechnology, liquid biopsy, and optical imaging which facilitate higher accuracy and implementation of personalized therapeutic modalities is covered. It clearly shows how these therapies affect the entire treatment system and touches upon the necessity of the multidisciplinary approach as well as the data validity and integration. Keywords: Brain tumors, Imaging modalities, Machine learning algorithms, Emerging technologies, MRI, CT, PET, DTI, fMRI, CNNs, SVMs, Random forests, Nanotechnology, Liquid biopsies, Optical imaging
1. INTRODUCTION Brain tumors are a worldwide concern that account for more than the 700,000 new cases diagnosed globally each year ([13]). Tumoral brain tissue is indeed complicated as well as the complicated structure of brain anatomy and its function which brings to light the immeasurable significance of timely and correct diagnosis for patients. Modern equipment has significantly shrunk on the time required to diagnose brain tumors. Magnetic resonance imaging (MRI) for now, is still the standard of care for brain tumors due to its intact cellular architecture and multimodality, thus giving us a better understanding of the microstructure and the vasculature of the tumor [6]. Computed tomography (CT), especially power spectrum CT, allows rapid and comprehensive evaluation of intracranial lesions. It is useful for large image files as it shows high performance and classification [23]. New technologies such as nanotechnology, liquid biopsy and optical imaging are expected to increase the sensitivity and specificity of tumor detection and provide non-invasive methods and targets for tumor evaluation and monitoring [24]. Difficulties remain in translating this technology into daily practice. Design of the imaging system, validation of new biomarkers, and integration of multimodal data are also important to increase the accuracy of diagnosis and clinical use. Additionally, clinical validation and use of technology in brain cancer research requires rigorous testing in real-world settings, highlighting the importance of collaborative research [22]. This literature review is designed to provide an overview of the latest advances in brain cancer diagnosis from 2020 to 2024 and examine the use of advanced assessment model, machine learning algorithms, and new technologies in brain diagnosis.
2. LITERATURE REVIEW Brain tumor detection has greatly improved brain diagnosis thanks to the integration of imaging models, machine learning algorithms, and new technologies. Imaging techniques such as MRI, CT, PET, DTI and fMRI increase the accuracy of diagnosis by capturing changes in tumors and white matter. Machine learning, especially deep learning such as CNN, can classify and categorize image data. Transfer learning and learning integration demonstrate the power of classification and interpretation of brain tumors. New technologies such as nanotechnology, liquid biopsy and optical therapy offer new ways to improve the brain's ability to see and control vision. However, challenges and research gaps remain, such as the design of imaging techniques and the validity of emerging biomarkers.
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