Comparative Study of Tumor Detection Techniques with their Suitability for Brain MRI Images

Background: This paper endeavors to identify an expedient approach for the detection of the brain tumor in MRI images. The detection of tumor is based on i) review of the machine learning approach for the identification of brain tumor and ii) review of a suitable approach for brain tumor detection. Discussion: This review focuses on different imaging techniques such as X-rays, PET, CT- Scan, and MRI. This survey identifies a different approach with better accuracy for tumor detection. This further includes the image processing method. In most applications, machine learning shows better performance than manual segmentation of the brain tumors from MRI images as it is a difficult and time-consuming task. For fast and better computational results, radiology used a different approach with MRI, CT-scan, X-ray, and PET. Furthermore, summarizing the literature, this paper also provides a critical evaluation of the surveyed literature which reveals new facets of research. Conclusion: The problem faced by the researchers during brain tumor detection techniques and machine learning applications for clinical settings have also been discussed.

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A Comparative Study on Face Detection Techniques for Security Surveillance

International Journal of Security Technology for Smart Device ◽

10.21742/ijstsd.2015.2.1.01 ◽

2015 ◽

Vol 2 (1) ◽

pp. 1-10

Author(s):

Dimple Chawla ◽

◽

Munesh Chandra Trivedi ◽

Keyword(s):

Comparative Study ◽

Face Detection ◽

Detection Techniques ◽

Security Surveillance

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A Comparative Study of AI-Based Intrusion Detection Techniques in Critical Infrastructures

ACM Transactions on Internet Technology ◽

10.1145/3406093 ◽

2021 ◽

Vol 21 (4) ◽

pp. 1-22

Author(s):

Safa Otoum ◽

Burak Kantarci ◽

Hussein Mouftah

Keyword(s):

Reinforcement Learning ◽

Intrusion Detection ◽

Comparative Study ◽

Performance Metrics ◽

Action Learning ◽

Smart Devices ◽

Critical Infrastructures ◽

State Action ◽

Detection Techniques ◽

Depth Analysis

Volunteer computing uses Internet-connected devices (laptops, PCs, smart devices, etc.), in which their owners volunteer them as storage and computing power resources, has become an essential mechanism for resource management in numerous applications. The growth of the volume and variety of data traffic on the Internet leads to concerns on the robustness of cyberphysical systems especially for critical infrastructures. Therefore, the implementation of an efficient Intrusion Detection System for gathering such sensory data has gained vital importance. In this article, we present a comparative study of Artificial Intelligence (AI)-driven intrusion detection systems for wirelessly connected sensors that track crucial applications. Specifically, we present an in-depth analysis of the use of machine learning, deep learning and reinforcement learning solutions to recognise intrusive behavior in the collected traffic. We evaluate the proposed mechanisms by using KDD’99 as real attack dataset in our simulations. Results present the performance metrics for three different IDSs, namely the Adaptively Supervised and Clustered Hybrid IDS (ASCH-IDS), Restricted Boltzmann Machine-based Clustered IDS (RBC-IDS), and Q-learning based IDS (Q-IDS), to detect malicious behaviors. We also present the performance of different reinforcement learning techniques such as State-Action-Reward-State-Action Learning (SARSA) and the Temporal Difference learning (TD). Through simulations, we show that Q-IDS performs with detection rate while SARSA-IDS and TD-IDS perform at the order of .

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A Comparative Study of Alzheimer Detection Techniques

International Journal for Research in Applied Science and Engineering Technology ◽

10.22214/ijraset.2021.37882 ◽

2021 ◽

Vol 9 (8) ◽

pp. 2877-2883

Author(s):

Adwait Patil

Keyword(s):

Machine Learning ◽

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Deep Learning ◽

Comparative Study ◽

Fuzzy System ◽

Early Stage ◽

Machine Learning Algorithms ◽

Support Vector ◽

Detection Techniques

Abstract: Alzheimer’s disease is one of the neurodegenerative disorders. It initially starts with innocuous symptoms but gradually becomes severe. This disease is so dangerous because there is no treatment, the disease is detected but typically at a later stage. So it is important to detect Alzheimer at an early stage to counter the disease and for a probable recovery for the patient. There are various approaches currently used to detect symptoms of Alzheimer’s disease (AD) at an early stage. The fuzzy system approach is not widely used as it heavily depends on expert knowledge but is quite efficient in detecting AD as it provides a mathematical foundation for interpreting the human cognitive processes. Another more accurate and widely accepted approach is the machine learning detection of AD stages which uses machine learning algorithms like Support Vector Machines (SVMs) , Decision Tree , Random Forests to detect the stage depending on the data provided. The final approach is the Deep Learning approach using multi-modal data that combines image , genetic data and patient data using deep models and then uses the concatenated data to detect the AD stage more efficiently; this method is obscure as it requires huge volumes of data. This paper elaborates on all the three approaches and provides a comparative study about them and which method is more efficient for AD detection. Keywords: Alzheimer’s Disease (AD), Fuzzy System , Machine Learning , Deep Learning , Multimodal data

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