Detection of intelligence from MRI images using a combination of long short-term memory network (LSTM) and fuzzy logic (FL)

Motamed, Sara; Eghdami Arabani, Majid

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Volume 23, Issue 1 (6-2026) JSDP 2026, 23(1): 51-60 | Back to browse issues page

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Motamed S, Eghdami Arabani M. Detection of intelligence from MRI images using a combination of long short-term memory network (LSTM) and fuzzy logic (FL). JSDP 2026; 23 (1) : 4
URL: http://jsdp.rcisp.ac.ir/article-1-1443-en.html

Detection of intelligence from MRI images using a combination of long short-term memory network (LSTM) and fuzzy logic (FL)

Sara Motamed ^*

, Majid Eghdami Arabani

Fouman, Iran

Abstract: (7 Views)

Intelligence has long been an interesting and important topic in psychology and cognitive science. It is considered a basic measure of a person's cognitive abilities, which include various aspects of reasoning, problem solving, memory, and general intellectual ability. Considering the importance of IQ in cognitive and psychological evaluations, the main goal of this article is to provide a new and effective approach to improve the accuracy of this measure through complex brain data processing. In this paper, we have analyzed and developed a hybrid model of LSTM networks and fuzzy inference layer in order to detect IQ using brain MRI images. By combining the deep learning power of LSTM in understanding temporal patterns and fuzzy inference capabilities in managing uncertainty, our proposed model succeeded in increasing the accuracy in identifying the level of intelligence compared to the basic methods. The results of the experiments showed that the accuracy of the model was significantly better than traditional techniques, and this indicates the high capabilities of the model in interpreting complex medical data. By examining the results, we find that the accuracy of the proposed model is better than other methods with a detection rate of 83%.

Article number: 4

Keywords: IQ, deep learning, brain MRI images, LSTM network, fuzzy inference engine.

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Type of Study: Research | Subject: Paper
Received: 2024/10/10 | Accepted: 2026/02/8 | Published: 2026/06/21 | ePublished: 2026/06/21

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