A Tuned Whale Optimization-Based Stacked-LSTM Network for Digital Image Segmentation

Rath, Patitapaban; Mallick, Pradeep Kumar; Tripathy, Hrudaya Kumar; Mishra, Debahuti

doi:10.1007/s13369-022-06964-6

Cited by 3 publications

(1 citation statement)

References 59 publications

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“…In medical imaging application, the stacked LSTM network is more prone to exploding and vanishing gradient problems. This normalization process reduces these problems by effectively maintaining activations (sigmoid and tangent) in a specific scale [27], [28]. During the back-propagation process, the normalization simplifies the propagation of gradients.…”

Section: Skin Lesion Classificationmentioning

confidence: 99%

Accurate detection of melanoma skin cancer using fuzzy based SegNet model and normalized stacked LSTM network

Chembian,

Sankar,

Koteeswaran

et al. 2024

IJEECS

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Early detection of melanoma skin cancer (MSC) is critical in order to prevent deaths from fatal skin cancer. Even though the modern research methods are effective in identifying and detecting skin cancer, it is a challenging task due to a higher level of color similarity between melanoma non-affected areas and affected areas, and a lower contrast between the skin portions and melanoma moles. For highlighting the aforementioned problems, an efficient automated system is proposed for an early diagnosis of MSC. Firstly, dermoscopic images are collected from two benchmark datasets namely, international skin imaging collaboration (ISIC)-2017 and PH2. Next, skin lesions are segmented from dermoscopic images by implementing a fuzzy based SegNet model which is a combination of both deep fuzzy clustering algorithm and the SegNet model. Then, hybrid feature extraction (ResNet-50 model and local tri-directional pattern (LTriDP) descriptor) is performed to capture the features from segmented skin lesions. These features are given into the normalized stacked long short-term memory (LSTM) network to categorize the classes of skin lesions. The empirical evaluation reveals that the proposed normalized stacked LSTM network achieves 98.98% and 98.97% of accuracy respectively on the ISIC2017 and PH2 datasets, and these outcomes are more impressive than those of the conventional detection models.

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Section: Skin Lesion Classificationmentioning

confidence: 99%

Accurate detection of melanoma skin cancer using fuzzy based SegNet model and normalized stacked LSTM network

Chembian,

Sankar,

Koteeswaran

et al. 2024

IJEECS

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OrientationJ Algorithm for Enhancing Printed Arabic Character Analysis

Yuadi,

Nihaya,

Pratiwi

et al. 2024

2024 16th International Conference on Information Technology and Electrical Engineering (ICITEE)

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A Ship Trajectory Prediction Model Based on Attention-BILSTM Optimized by the Whale Optimization Algorithm

Jia

Yang

et al. 2023

Applied Sciences

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Nowadays, maritime transportation has become one of the most important ways of international trade. However, with the increase in ship transportation, the complex maritime environment has led to frequent traffic accidents, causing huge economic losses and safety hazards. For ships in maritime transportation, collision avoidance and route planning can be achieved by predicting the ship’s trajectory, which can give crews warning to avoid dangers. How to predict the ship’s trajectory more accurately is of great significance for risk avoidance. However, existing ship trajectory prediction models suffer from problems such as poor prediction accuracy, poor applicability, and difficult hyperparameter design. To address these issues, this paper adopts the Bidirectional Long Short-Term Memory (BILSTM) model as the base model, as it considers contextual information of time-series data more comprehensively. Meanwhile, to improve the accuracy and fitness of complex ship trajectories, this paper adds an attention mechanism to the BILSTM model to improve the weight of key information. In addition, to solve the problem of difficult hyperparameter design, this paper optimizes the hyperparameters of the Attention-BILSTM network by fusing the Whale Optimization Algorithm (WOA). In this paper, the AIS data are filtered, and the trajectory is complemented by the cubic spline interpolation method. Using the pre-processed AIS data, this WOA-Attention-BILSTM model is compared and assessed with traditional models. The results show that compared with other models, the WOA-Attention-BILSTM prediction model has high prediction accuracy, high applicability, and high stability, which provides an effective and feasible method for ship collision avoidance, maritime surveillance, and intelligent shipping.

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A Tuned Whale Optimization-Based Stacked-LSTM Network for Digital Image Segmentation

Cited by 3 publications

References 59 publications

Accurate detection of melanoma skin cancer using fuzzy based SegNet model and normalized stacked LSTM network

Accurate detection of melanoma skin cancer using fuzzy based SegNet model and normalized stacked LSTM network

OrientationJ Algorithm for Enhancing Printed Arabic Character Analysis

A Ship Trajectory Prediction Model Based on Attention-BILSTM Optimized by the Whale Optimization Algorithm

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