Forthcoming Articles

International Journal of Signal and Imaging Systems Engineering

International Journal of Signal and Imaging Systems Engineering (IJSISE)

Forthcoming articles have been peer-reviewed and accepted for publication but are pending final changes, are not yet published and may not appear here in their final order of publication until they are assigned to issues. Therefore, the content conforms to our standards but the presentation (e.g. typesetting and proof-reading) is not necessarily up to the Inderscience standard. Additionally, titles, authors, abstracts and keywords may change before publication. Articles will not be published until the final proofs are validated by their authors.

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International Journal of Signal and Imaging Systems Engineering (3 papers in press)

Regular Issues

  • A Fusion Framework for Melanoma Detection using SkCanNet Model and Hand-Crafted Features   Order a copy of this article
    by Apurva Shinde, Sangita Chaudhari 
    Abstract: Skin cancer remains a major global health concern, emphasising the need for early and accurate diagnosis. Computer-aided diagnosis (CAD) has been applied for skin cancer detection. A critical issue in skin cancer detection is the detection and removal of artefacts like hair and noise from lesion images. Existing hair removal techniques often fail to preserve lesion details, affecting classification accuracy. To enhance melanoma detection, an advanced pre-processing method is necessary while preserving lesion features with integration of deep learning with handcrafted features. This paper presents an advanced pre-processing technique using Modified E-shaver, Modified Dull Razor, and Adaptive Principal Curvature. SkCanNet, is proposed for feature extraction which is combined with standard classifiers like KNN, SVM, and logistic regression for classification. The proposed approach significantly enhances skin cancer detection accuracy, specificity, sensitivity, and F1-score. Compared to existing deep learning models proposed system presents a promising solution for early melanoma detection.
    Keywords: Melanoma Skin cancer; Gray-Level Co-Occurrence Matrix; Local Binary Pattern; SkCanNet Architecture.
    DOI: 10.1504/IJSISE.2026.10074889
     
  • Fault Detection of Industrial Robotic Arms based on Joint Tracking   Order a copy of this article
    by X.U. Yue, Yinlong Zhang, Xin Wang, Chu Wang 
    Abstract: To ensure robotic system safety and reliability, accurate tracking of robotic arm and human joint positions and real-time monitoring of joint changes and early signs of wear or loosening are essential. Traditional methods suffer from limited accuracy and high maintenance costs in complex environments due to similar components, occlusion, and complicated backgrounds. To address these challenges, this paper proposes a fault detection method for robotic arms in complex scenarios that enhances joint identification and early warning capabilities. The approach integrates colour detection, geometric perception, the Detect DBB module, and the integrated time processing (ITP) module. Leveraging deep feature learning, it enables real-time trajectory monitoring and precise identification of early wear and loosening. Experiments demonstrate that the proposed method improves detection accuracy, reduces unplanned downtime, extends the robotic arms service life, and lowers maintenance costs, thus validating its effectiveness and practical value.
    Keywords: robotic arm fault detection; joint tracking; preventive maintenance; human-robot interaction; color detection; geometric perception.
    DOI: 10.1504/IJSISE.2025.10074919
     
  • Lung Disease Classification using NF-RVFL Optimised by IROA with HybridGNet Segmentation and DIAG Augmented Data in chest X-Ray Analysis   Order a copy of this article
    by C.H. Ashok Babu, J. Pandu, Ravi Shankar Reddy Gosula 
    Abstract: Lung diseases like lung opacity, pneumonia, and COVID-19 pose major health challenges, creating a need for more advanced diagnostic solutions. Existing methods suffer from high space requirements, data imbalance, long processing times, and deep learning (DL) limitations like vanishing gradients and increased misclassification. To solve this, a novel lung disease classification framework is proposed that integrates data augmentation, segmentation, and classification. The diffusion-based In-Distribution Anomaly Generation (DIAG) pipeline uses multimodal latent diffusion models to generate realistic anomalous images, effectively resolving data imbalance. For segmentation, HybridGNet model combines convolutional neural networks (CNNs) with graph convolutional neural networks to accurately decode anatomical structures. Classification is performed using neuro-fuzzy random vector functional link (NF-RVFL) model, which employs an interpretable IF-THEN decision structure. Its parameters are optimised using Improved Remora Optimization Algorithm, reducing complexity and enhancing performance. Experimental results show an accuracy of 0.989, outperforming existing methods and demonstrating strong potential for precise lung disease classification.
    Keywords: Classification; segmentation; Data augmentation; Chest X-Ray; Lung disease.
    DOI: 10.1504/IJSISE.2025.10074986
     

 

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