International Journal of Signal and Imaging Systems Engineering (4 papers in press)

Regular Issues

• Lung Disease Classification using NF-RVFL Optimised by IROA with HybridGNet Segmentation and DIAG Augmented Data in chest X-Ray Analysis  
  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
   
  
• A Combined use of Wavelet Packet Decomposition and Convolutional Neural Network for Robust Bearing Fault Diagnosis  
  by Adel Boulassel, Hocine Bendjama, Mohamed Larbi Mihoub 
  Abstract: Rotating machines play a vital role in industrial systems, where unplanned downtime can lead to significant financial and operational losses. Rolling bearings, essential for supporting rotational motion, are prone to faults such as misalignment, imbalance, and wear, making their monitoring crucial for predictive maintenance. This paper proposes a novel fault diagnosis method combining wavelet packet decomposition (WPD) with a one-dimensional convolutional neural network (1D-CNN). WPD decomposes vibration signals to extract timefrequency fault features, from which statistical indicators such as kurtosis, energy, and crest factor are calculated. A new fault-sensitive indicator, defined as the product of these metrics, selects the most informative component for fault detection. The reconstructed signal is then classified using a 1D-CNN, achieving excellent diagnostic accuracy under varying loads. Compared with Random Forest (RF) model, the proposed method better captures non-stationary features and enhances classification performance, offering a robust solution for rotating machinery fault diagnosis.
  Keywords: Bearing faults diagnosis; wavelet packet decomposition; convolutional neural network; wind turbine; rotating machines.
  DOI: 10.1504/IJSISE.2025.10075044
   
  
• Robust Cotton Leaf Disease Classification Using Hybrid Deep Learning Approach with Multi- Loss Function  
  by Sailaja Madhu, V. Ravi Sankar 
  Abstract: This paper presents an advanced deep learning framework for the early detection of cotton leaf diseases. Pre-processing is performed using an adaptive median filter to remove noise from input images, enhancing clarity and quality. The improved images are then processed by a hybrid model combining the CN2-SE module and a modified Inception V3 network. The CN2-SE module extracts features, while the modified Inception V3 network also extracts complementary features. These features are merged into a unified set and passed through a multi-layer perceptron (MLP) for classification, which employs a hybrid multi-loss function to ensure improved accuracy while preventing overfitting. Tailored to address both sustainability challenges and global cotton demands, this approach achieves an outstanding classification accuracy of 99.41%, with strong performance in sensitivity, specificity, and recall. By advancing precision agriculture, this research provides an effective solution for automating cotton crop management and enhancing productivity and sustainability in cotton farming.
  Keywords: Robust Cotton leaf disease; CN2-SE; multi-layer perceptron with a multi-loss function; bacterial blight; leaf curl virus; and various fungal infections.
  DOI: 10.1504/IJSISE.2025.10075940
   
  
• An Intelligent Cancer Image Classification Framework Using Region-Vision Transformer-based Adaptive Multiscale EfficientNetB7 with Gated Recurrent Unit Layer  
  by Vipul Gajjar, Kamalesh V. N, Kavitha Rani Balmuri 
  Abstract: Cancer is a harmful disorder, so detecting cancer in the beginning stages helps to save human life. But, detecting the type of cancer by manual methods is difficult. Conventional techniques perform separate procedures for feature learning and dimensionality reduction. However, these processes need more time. An adaptive deep learning approach is developed to solve the limitations of the conventional cancer image classification process. The required images for performing the classification process are collected from the standard resources. After collecting the images, it are directly passed to the Region-Vision Transformer-based Adaptive Multi-scale EfficientNetB7 with Gated Recurrent Unit Layer (RViT-AMEB7-GRU) for cancer image classification, in which the attributes of the RViT-AMEB7-GRU are tuned using the Updated Random Function-aware Good, the Bad, and the Ugly Optimizer (URF-GBUO). The developed model is adopted by clinicians for detecting cancer at an earlier stage. Finally, the experimentation is done on the proposed model to prove effectiveness.
  Keywords: Cancer Classification; Medical Images; Region-Vision Transformer-based Adaptive Multiscale EfficientNetB7 with Gated Recurrent Unit Layer.
  DOI: 10.1504/IJSISE.2026.10076704