Recent Methods and Challenges in Brain Tumor Detection Using Medical
Image Processing

Manikandan, V. M.; Kandimalla, Sai Yasheswini; Vamsi, Dhara Mohana; Bhavani, Samudrala

doi:10.2174/1872212117666220823100209

Cited by 5 publications

(3 citation statements)

References 28 publications

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“…77 Additionally, the complexity of the brain as an organ and the critical nature of brain tumors contribute to the challenge of early detection and diagnosis. 78 Further improvement is required in the efficiency of existing detection schemes, and critical research challenges need to be addressed in order to develop new methods for brain tumor detection. 79 Overall, the challenges in explaining brain tumor detection stem from the unique requirements and complexities of the healthcare domain, necessitating specialized techniques and approaches.…”

Section: Challenges In Explaining Brain Tumor Detectionmentioning

confidence: 99%

Current challenges of the state-of-the-art of AI techniques for diagnosing brain tumor

MO,

2023

MSEIJ

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Background: Brain is the control center of the human body, in recent time, different variety of brain diseases are being discovered. The brain disease diagnosis tools are becoming challenging and still an open area of research, application of AI in brain disease diagnosis has made disease prediction and detection more precise and accurate. Automated technologies for non-invasive analysis of brain images have become necessary, because disease of brain is fatal and are the cause of large number of deaths in developed countries. Brain tumor surgery augmented with AI can result in safer and more effective treatment. The knowledge gap between clinical and data science experts still presents significant challenges. This paper will review literatures related to current challenges of AI technologies for brain tumor diagnosis and suggest new directions of AI technologies for diagnosing brain tumour. A systematic search of major academic databases (such as Science Direct, IEEE explore digital Library, and Google scholar) was conducted to identify relevant studies published between 2015 and 2023. The search term used in this study include “Brain tumor Diagnosis”, “AI challenges in Brain tumor Diagnosis”, ‘AI techniques” and “AI challenges in medicine and future”. Studies were included if they utilized AI techniques for brain tumor diagnosis. The identified studies were evaluated for the key challenges they encountered in their diagnostic approaches. The Present study identified several challenges related to the application of AI techniques in brain tumour diagnosis. These challenges include: Interpretability and explainability, variations in tumour location, shape, and size which make accurate segmentation and classification difficult. Overall, the challenges in explaining brain tumor detection stem from the unique requirements and complexities of the healthcare domain, necessitating specialized techniques and approaches. This study summarizes the new directions for AI as (I) Data Hungry: Large, standardized, annotated data sets and excellent ground truth data are necessary for the development of accurate AI. (II) Radiomics: makes it possible to extract a vast number of quantitative features from intricate clinical imaging arrays and convert them into high-dimensional data that can be further processed to determine their relationship to the histological features of the tumor, which represent underlying genetic mutations and malignancy as well as grade, progression, response to therapy, and even overall survival (OS). (III) Black box: AI, for instance, is capable of predicting the best course of care for a patient, but it is unable to explain its reasoning. A trend toward easing this restriction is interpretable deep learning, (IV) Demonstrating the generalizability of deep learning applications and conducting external validation are two major obstacles. (V) There are knowledge gaps in clinical oncology that need to be filled in order to successfully integrate AI and maximize its effects. (VI) Several national professional bodies have started programs to bridge these knowledge gaps and advance the adoption of AI in oncology in response to these difficulties.

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Section: Challenges In Explaining Brain Tumor Detectionmentioning

confidence: 99%

Current challenges of the state-of-the-art of AI techniques for diagnosing brain tumor

MO,

2023

MSEIJ

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“…The acquisition of a brain tumor biopsy necessitates surgical intervention, hence rendering the procedure more intricate compared to biopsies conducted on other organs. Therefore, it is imperative to identify a dependable substitute for invasive surgical techniques in the realm of diagnostic procedures [9], [10]. When it comes to the identification of brain tumors, magnetic resonance imaging (MRI) is considered the most reliable and widely accepted diagnostic technique.…”

Section: Introductionmentioning

confidence: 99%

“…As data sets expand, it becomes harder to draw meaningful conclusions from them. It is more time-consuming and expensive to look for a brain tumor manually [9], [13]. Thus far, the application of computer-aided technologies for medical image processing has proven to be progressively advantageous for physicians in the detection of brain malignancies.…”

Section: Introductionmentioning

confidence: 99%

Automated classification of brain tumor-based magnetic resonance imaging using deep learning approach

Owida,

AlMahadin,

Al-Nabulsi

et al. 2024

IJECE

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The treatment of brain tumors poses significant challenges and contributes to a significant number of deaths on a global scale. The process of identifying brain tumors in medical practice involves the visual analysis of photographs by healthcare experts, who manually delineate the tumor locations. However, this approach is characterized by its time-consuming nature and susceptibility to errors. In recent years, scholars have put forth automated approaches to early detection of brain tumors. However, these techniques face challenges attributed to their limited precision and significant false-positive rates. There is a need for an effective methodology to identify and classify tumors, which involves extracting reliable features and achieving precise disease classification. This work presents a novel model architecture that is derived from the EfficientNetB3. The suggested framework has been trained and assessed on a dataset consisting of 7,023 magnetic resonance images. The findings of this study indicate that the fused feature vector exhibits superior performance compared to the individual vectors. Furthermore, the technique that was provided showed superior performance compared to the currently available systems and attained a 100% accuracy rate. As a result, it is viable to employ this technique within a clinical environment for the purpose of categorizing brain tumors based on magnetic resonance images scans.

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Comparative Analysis of Deep Learning and Machine Learning for Detection and Classification of Brain Tumors at Multiple Stages

Ramprakash,

Hari,

Kumar

et al. 2024

2024 Second International Conference on Emerging Trends in Information Technology and Engineering (ICETITE)

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Recent Methods and Challenges in Brain Tumor Detection Using Medical Image Processing

Cited by 5 publications

References 28 publications

Current challenges of the state-of-the-art of AI techniques for diagnosing brain tumor

Current challenges of the state-of-the-art of AI techniques for diagnosing brain tumor

Automated classification of brain tumor-based magnetic resonance imaging using deep learning approach

Comparative Analysis of Deep Learning and Machine Learning for Detection and Classification of Brain Tumors at Multiple Stages

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