Optimal Attention Deep Learning-Based Multi-Class Emotion Detection and Classification Model on fMRI Images

References	Years	Research gaps	Methodologies	Outcomes
[16]	2025	Current methods lack simple, fast use of spatial-temporal features for real-time ED.	SVM, KNN approaches.	Recognition rates of 94.65%, 93.98%, and 75.59%.
[17]	2025	FER methods lack thermal-based deep models for reliable, lighting-independent, accurate emotion recognition.	EfficientNet, GWO, SVM, KNN, RF, and Gradient Boosting techniques.	Accuracy of 91.42% and 99.48%.
[18]	2025	CNN-based emotion recognition struggles with imbalanced data and unreliable real-time performance across environments today.	CNN models.	Accuracy of 97%.
[19]	2025	Existing IER methods ignore progressive emotion perception and lack mechanisms to model emotions.	ViT and DPMN.	Accuracy of 75.35% and 66.84%.
[20]	2024	Existing methods lack concept-guided attention to accurately identify truly emotion-relevant regions in images.	CMANet model.	Gains an improved solution.
[21]	2023	Existing IEC methods cannot unify different emotion models or handle complex semantic relationships effectively.	Unified generative framework.	Achieves a better outcome.
[22]	2022	Existing methods overlook broad contextual features around affective regions, limiting robust emotion representation learning.	GCN, GAT, and RPN models.	Recognition rate of 87.47%, 76.05%, and 76.50%.
[23]	2024	CNN models still suffer from limited accuracy and lack deeper feature extraction for complex image classification tasks.	CNN techniques.	Accuracy of 62.2%.

[i] CMAN, Concept-guided Multi-level Attention Network; CNN, Convolutional Neural Network; ED, Emotion Detection; GCN, Graph Convolutional Network; GWO, Gray Wolf Optimizer; RF, Random Forest; SVM, Support Vector Machine.

High-level workflow of the proposed OADL-MCEDC approach. fMRI, functional magnetic-resonance image; OADL-MCEDC, Optimal Attention Deep Learning-based Multi-class Emotion Detection and Classification.

Architecture of hybrid capsule network. FC, fully connected.

Table 2:

Classifier recognition of OADL-MCEDC method on various epochs

Class labels	Accur_y	Preci_n	Recal_l	F_measure	MCC
Epoch – 500 for multi-classes (angry, blank, happy, neutral, sad, scrambled)	94.73	81.40	88.67	84.88	81.80
	95.40	85.77	86.80	86.28	83.52
	93.58	76.96	87.73	81.99	78.35
	96.80	90.95	89.73	90.34	88.42
90.71	74.85	66.67	70.52	65.18
94.02	86.38	76.13	80.94	77.62
Average
94.21	82.72	82.62	82.49	79.15
Epoch – 1,000 for multi-classes (angry, blank, happy, neutral, sad, scrambled)	95.82	84.69	91.47	87.95	85.52
	97.09	90.89	91.73	91.31	89.56
	95.80	84.59	91.47	87.89	85.45
	97.33	92.68	91.20	91.94	90.34
93.04	81.08	76.00	78.46	74.37
95.76	91.04	82.67	86.65	84.27
Average
95.81	87.49	87.42	87.37	84.92
Epoch – 1,500 for multi-classes (angry, blank, happy, neutral, sad, scrambled)	96.44	86.78	92.80	89.69	87.62
	97.38	91.69	92.67	92.18	90.60
	97.29	89.55	94.80	92.10	90.52
	97.84	94.42	92.53	93.47	92.18
95.11	87.32	82.67	84.93	82.06
96.64	92.72	86.67	89.59	87.67
Average
96.79	90.41	90.36	90.33	88.44
Epoch – 2,000 for multi-classes (angry, blank, happy, neutral, sad, scrambled)	96.67	88.07	92.53	90.25	88.28
	97.56	92.33	93.07	92.70	91.23
	97.80	91.68	95.47	93.53	92.24
	97.87	94.07	93.07	93.57	92.29
95.49	87.21	85.47	86.33	83.64
97.11	94.41	87.87	91.02	89.39
Average
97.08	91.30	91.24	91.23	89.51
Epoch – 2,500 for multi-classes (angry, blank, happy, neutral, sad, scrambled)	97.51	91.00	94.40	92.67	91.19
	98.38	94.60	95.73	95.16	94.19
	98.51	93.06	98.40	95.66	94.81
	98.49	96.58	94.27	95.41	94.52
97.29	93.85	89.60	91.68	90.09
98.00	95.71	92.13	93.89	92.71
Average
98.03	94.14	94.09	94.08	92.92
Epoch – 3,000 for multi-classes (angry, blank, happy, neutral, sad, scrambled)	97.93	93.05	94.67	93.85	92.62
	98.62	95.62	96.13	95.88	95.05
	98.62	93.99	98.00	95.95	95.15
	98.73	97.14	95.20	96.16	95.41
97.84	94.54	92.40	93.46	92.18
98.24	95.77	93.60	94.67	93.63
Average
98.33	95.02	95.00	95.00	94.01

[i] MCC, Matthews correlation coefficient; OADL-MCEDC, Optimal Attention Deep Learning based Multi-class Emotion Detection and Classification.

Table 3:

Details of dataset

Emotions	Sample images
Angry	750
Blank	750
Happy	750
Neutral	750
Sad	750
Scrambled	750
Total Images	4,500

Preprocessed images, feature maps, and GradCAM visualizations.

Confusion matrices of OADL-MCEDC method **(A–F)** 500–3,000 epochs. OADL-MCEDC, Optimal Attention Deep Learning based Multi-class Emotion Detection and Classification.

**(A–F)** Average outcomes of OADL-MCEDC methodology on numerous epochs. OADL-MCEDC, Optimal Attention Deep Learning based Multi-class Emotion Detection and Classification.

Accur_y curve of OADL-MCEDC approach on 3,000 epoch. OADL-MCEDC, Optimal Attention Deep Learning based Multi-class Emotion Detection and Classification.

Loss curve of OADL-MCEDC methodology on 3,000 epoch. OADL-MCEDC, Optimal Attention Deep Learning based Multi-class Emotion Detection and Classification.

PR curve of OADL-MCEDC approach on 3,000 epochs. OADL-MCEDC, Optimal Attention Deep Learning based Multi-class Emotion Detection and Classification; PR, precision-recall.

ROC curve of OADL-MCEDC system on 3,000 epoch. OADL-MCEDC, Optimal Attention Deep Learning based Multi-class Emotion Detection and Classification.

Table 4:

Comparative analysis of OADL-MCEDC methodology with existing approaches

Approach	Accur_y	Preci_n	Recal_l	F_measure
1D CNN	87.00	84.22	91.15	92.69
SVM	83.80	86.78	91.32	91.27
XGBoost	82.00	86.60	93.34	94.09
LDA	79.00	94.74	93.09	85.75
RF	91.00	83.03	84.95	93.91
DNN	87.34	85.00	92.11	93.26
Decision tree	71.99	91.85	82.83	82.62
OADL-MCEDC	98.33	95.02	95.00	95.00

[i] CNN, convolutional neural network; DNN, deep neural network; OADL-MCEDC, Optimal Attention Deep Learning based Multi-Class Emotion Detection and Classification, RF, random forest; SVM, support vector machine; XGBoost, Extreme Gradient Boosting.

Comparative analysis of OADL-MCEDC method **(A)** accur_y, **(B)** preci_n, **(C)** recal_l, and **(D)** F_measure. CNN, convolutional neural network; DNN, deep neural network; OADL-MCEDC, Optimal Attention Deep Learning based Multi-class Emotion Detection and Classification; SVM, support vector machine.

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DOI: https://doi.org/10.2478/ijssis-2026-0053 | Journal eISSN: 1178-5608

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Language: English

Submitted on: Jan 31, 2026

Published on: Aug 12, 2026

Published by: International Journal on Smart Sensing and Intelligent Systems

In partnership with: Paradigm Publishing Services

Publication frequency: 1 issue per year

Keywords:

emotion detection,

fMRI images,

hybrid capsule network,

deep learning,

classification,

hyperparameter tuning

Related subjects:

Engineering,

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© 2026 L. Dinesh, G. Indirani, published by International Journal on Smart Sensing and Intelligent Systems
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Volume 19 (2026): Issue 1 (January 2026)

Optimal Attention Deep Learning-Based Multi-Class Emotion Detection and Classification Model on fMRI Images

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