Enhanced Skill Optimization Algorithm and Stacked Long Short-Term Memory with Sech Activation Function for Gastrointestinal Disease

Janagama Srividya; Harikrishna Bommala

doi:10.2478/ijssis-2026-0030

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Enhanced Skill Optimization Algorithm and Stacked Long Short-Term Memory with Sech Activation Function for Gastrointestinal Disease

International Journal on Smart Sensing and Intelligent Systems

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

By: Janagama Srividya and Harikrishna Bommala

Open Access

|May 2026

Figures & Tables

Workflow of proposed methodology. AHE, adaptive histogram equalization; ESOA, enhanced skill optimization algorithm; Stacked LSTM-SAF, stacked long short-term memory with Sech activation function.

Workflow of the ESOA method. ESOA, enhanced skill optimization algorithm.

Algorithm 1:
Input: Dataset {Kvasir-V1 and Kvasir-V2}
Output: Predicted labels
Step 1: Preprocessing
For each image in the dataset
Apply AHE for contrast enhancement
Employ the Bilateral filter to minimize noise
End for
Step 2: Feature extraction
Extract features using DenseNet-201
Extract features using EfficientNet-B3
Concatenate and combine the feature vectors
Step 3: Feature selection
Initialize the population of candidate feature subsets
For iteration = 1 to MaxIteration do
For each candidate solution do
Evaluate the fitness function
End for
Generate mutated vectors using the mutation strategy
Update candidate solutions based on leader guidance using GTO during the exploration stage
Perform local search to refine selected features during the exploitation stage
End for
Select the best feature subset
Step 5: Classification
Define the Stacked LSTM model
Include multiple LSTM layers
Employ the Sech activation function
Apply Softmax for multiclass classification
Predicted label

Structure of the stacked LSTM. Stacked LSTM, stacked long short-term memory.

Sample images of actual and predicted labels **(A)** Kvasir-V1 **(B)** Kvasir-V2 dataset.

Table 1:

Performance analysis of different feature selection methods

Methods	Accuracy (%)	Precision (%)	Recall (%)	F1-score (%)	Specificity (%)
Kvasir-V1
OOA	94.17	94.54	93.92	94.23	92.67
COA	96.66	95.15	95.38	95.27	94.38
BES	97.37	95.12	96.30	95.71	94.79
SOA	97.99	96.53	97.98	97.25	96.95
ESOA	99.60	99.20	98.71	98.96	99.88
Kvasir-V2
OOA	94.26	94.65	93.33	94.65	93.33
COA	95.58	95.21	94.67	95.21	94.67
BES	97.51	95.18	96.84	95.18	96.84
SOA	97.51	97.18	96.84	97.18	96.84
ESOA	99.88	99.61	97.12	99.61	97.12
HyperKvasir
OOA	94.32	94.78	93.85	94.11	92.91
COA	96.28	95.42	95.16	95.29	94.63
BES	97.14	95.88	96.41	96.14	95.37
SOA	97.92	96.71	97.65	97.18	96.82
ESOA	99.74	99.33	98.95	99.14	99.61

[i] BES, bald eagle search optimizer; COA, coati optimization algorithm; ESOA, enhanced skill optimization algorithm; OOA, osprey optimization algorithm; SOA.

Table 2:

Performance analysis of different classification methods

Methods	Accuracy (%)	Precision (%)	Recall (%)	F1-score (%)
Kvasir-V1
RNN	89.44	90.51	88.90	91.42
GRU	93.80	94.03	93.59	92.14
LSTM	96.04	95.99	94.88	94.66
Stacked-LSTM	99.60	98.71	99.88	99.20
Kvasir-V2
RNN	90.56	90.50	91.27	92.95
GRU	94.07	93.10	92.00	94.56
LSTM	97.49	96.40	95.55	95.61
Stacked-LSTM	99.88	97.93	97.12	99.61
HyperKvasir
RNN	89.88	90.65	89.44	91.12
GRU	93.92	94.21	93.78	92.83
LSTM	96.38	96.10	95.21	95.68
Stacked-LSTM	99.74	98.95	99.33	99.14

[i] GRU, gated recurrent unit; LSTM, long short-term memory; RNN, recurrent neural network; Stacked LSTM, stacked long short-term memory.

Table 3:

Performance analysis of different activation functions

Methods	Accuracy (%)	Precision (%)	Recall (%)	F1-score (%)	T-test from p-values	CI (%)
Kvasir-V1
Stacked LSTM-ReLU	94.97	93.47	92.60	93.63	0.032	86.18
Stacked LSTM-Tanh	95.46	93.28	94.30	95.04	0.030	87.63
Stacked LSTM-Sigmoid	97.03	96.96	95.43	96.01	0.029	89.06
Stacked LSTM-sech	99.50	98.71	99.88	99.20	0.026	94.12
Kvasir-V2
Stacked LSTM-ReLU	93.55	92.53	92.67	91.76	0.034	89.60
Stacked LSTM-Tanh	96.62	95.71	96.00	97.26	0.030	90.17
Stacked LSTM-Sigmoid	97.45	95.50	94.18	96.65	0.028	91.78
Stacked LSTM-sech	99.88	97.93	97.12	99.61	0.024	94.36
HyperKvasir
Stacked LSTM-ReLU	94.76	93.62	92.89	93.45	0.036	87.15
Stacked LSTM-Tanh	95.84	94.71	95.25	95.07	0.034	89.36
Stacked LSTM-Sigmoid	97.28	96.64	95.81	96.22	0.031	91.58
Stacked LSTM-Sech	99.74	98.95	99.33	99.14	0.029	94.85

[i] CI, confidence interval; ReLU, rectified linear unit; Stacked LSTM, stacked long short-term memory.

Evaluation of confusion matrix for Stacked LSTM-SAF **(A)** Kvasir-V1, **(B)** Kvasir-V2, **(C)** HyperKvasir. Stacked LSTM-SAF, stacked long short-term memory with Sech activation function.

Evaluation of RoC curve for Stacked LSTM-SAF: **(A)** Kvasir-V1, **(B)** Kvasir-V2, **(C)** HyperKvasir. ROC, receiver operating characteristics; Stacked LSTM-SAF, stacked long short-term memory with Sech activation function.

Analysis of standard deviation for the proposed method: **(A)** Kvasir-V1, **(B)** Kvasir-V2, **(C)** HyperKvasir.

Table 4:

Performance analysis of computational complexity across datasets

Methods	Datasets	Memory consumption (MB)	Training time (s)	Inference time (s)
RNN	Kvasir-V1	27.12	37.95	36.74
GRU		23.05	32.58	30.84
LSTM		8.67	30.58	26.59
Stacked-LSTM-SAF		6.72	2.54	9.58
RNN	Kvasir-V2	27.48	38.62	37.12
GRU		22.87	35.12	31.04
LSTM		9.02	31.28	27.11
Stacked-LSTM-SAF		6.58	2.73	9.92
RNN	HyperKvasir	30.48	42.69	40.98
GRU		27.46	25.96	22.02
LSTM		21.69	19.78	17.36
Stacked-LSTM-SAF		8.46	5.80	11.96

[i] GRU, gated recurrent unit; LSTM, long short-term memory; MB, RNN, recurrent neural network; Stacked LSTM-SAF, stacked long short-term memory with Sech activation function.

Table 5:

Cross-dataset validation results: Trained on Kvasir-V1 and tested on Kvasir-V2

Methods	Accuracy (%)	Precision (%)	Recall (%)	F1-score (%)
RNN	87.35	88.20	86.50	87.34
GRU	91.12	92.05	90.50	91.27
LSTM	94.50	95.10	93.80	94.44
Stacked LSTM-SAF	97.25	96.80	96.00	96.39

[i] GRU, gated recurrent unit; LSTM, long short-term memory; RNN, recurrent neural network; Stacked LSTM-SAF, stacked long short-term memory with Sech activation function.

Table 6:

Comparative analysis of existing methods on Kvasir-V1 and V2 datasets

Methods	Datasets	Accuracy (%)	Precision (%)	Recall (%)	F1-score (%)
LPNet [17]	Kvasir-V1	93.55	93.55	93.55	93.55
VGG16 kernle RBF [19]	Kvasir-V2	96.64	97	97	97
SK-Net [20]	Kvasir-V1	98.45	N/A	96.60	N/A
	Kvasir-V2	97.83	N/A	N/A	N/A
Star-GAN + InceptionNet-V3 [21]	Kvasir-V2	94.96	N/A	94.93	94.93
CapsNet [22]	Kvasir-V2	93.40	N/A	N/A	N/A
Proposed ESOA with Stacked LSTM-SAF	Kvasir-V1	99.60	98.71	99.88	99.20
	Kvasir-V2	99.88	97.93	97.12	99.61

[i] ESOA, enhanced skill optimization algorithm; Stacked LSTM-SAF, stacked long short-term memory with Sech activation function; Star-GAN, star-generative adversarial network.

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DOI: https://doi.org/10.2478/ijssis-2026-0030 | Journal eISSN: 1178-5608

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Language: English

Submitted on: Aug 7, 2025

Published on: May 28, 2026

Published by: International Journal on Smart Sensing and Intelligent Systems

In partnership with: Paradigm Publishing Services

Publication frequency: 1 issue per year

Keywords:

deep learning,

enhanced skill optimization algorithm,

gastrointestinal disease,

Gorilla troop optimizer,

stacked long short-term memory with Sech activation function

Related subjects:

Engineering,

Introductions and overviews,

Engineering, other

© 2026 Janagama Srividya, Harikrishna Bommala, 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)

Enhanced Skill Optimization Algorithm and Stacked Long Short-Term Memory with Sech Activation Function for Gastrointestinal Disease

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