Accurately identification of brain tumors is challenging due to the limitations of single-modality imaging, which often struggles to provide diagnostic information. Image registration addresses this challenge by ensuring precise alignment of multimodal images, facilitating effective fusion for early brain disease diagnosis. However, several traditional image registration methods often struggle with complex anatomy and spectral-spatial distortions, leading to poor fusion quality. To address this challenge, a dual-step strategy is proposed for multimodal medical images to assist doctors in diagnosing diseases. During the initial stage, source and template image alignment is performed by VGG-19 to extract the features. Subsequently, dynamic inlier selection to optimize the feature matching process, thereby improving the robustness of registration. Finally, thin plate spline interpolation is used to compute the affine parameters, achieving accurate registration of source image and template image. In the second stage, fusion scheme is developed by employing lifting wavelet transform (LWT) and singular value decomposition (SVD). Specifically, LWT is applied to decompose the input images into multi-level frequency bands, including low-frequency (approximation) and high-frequency (detail) components. The fused wavelet coefficients are then reconstructed using inverse LWT to generate a final fused image with improved visual clarity and reliability. The effectiveness of the dual-step strategy is extensively evaluated on both monomodal and multimodal medical images from standard and real-world datasets. The results highlight notable enhancements in performance metric RMSE reduced from 0.2886 to 0.10543, SSIM improved from 0.7238 to 0.9312, PSNR climbed from 58.9262 to 68.81256, and CC increased from 0.9284 to 0.99731.

Plant disease identification and detection refer to the process of recognizing and diagnosing diseases that affect plants. This process is crucial for maintaining plant health, maximizing crop yields, and preventing the spread of diseases to neighbouring plants. Various techniques have been designed for plant disease identification with outstanding results. Still, it exhibits a prolonged convergence rate, taking much time to execute because of long iterations, consuming a large chunk of resources, and suffering local minimum problems. All these issues have emerged from the expansion of hidden layers and their parameters. In this work, a novel hybrid approach is proposed using a ResNet-50 based deep neural network integrated with a Kernel Extreme Learning Machine (KELM) classifier for efficient and accurate plant disease classification. Unlike conventional deep learning methods that rely on iterative training and heavy resources, our approach uses a non-iterative, single-pass KELM classifier to significantly reduce computational complexity while maintaining high classification performance. The method extracts deep, discriminative features via ResNet-50 and feeds them into a lightweight KELM for final classification. The performance of the proposed method is matched with the individual performance of ResNet50 and KELM, along with other Deep Learning (DL) frameworks. Experiments are conducted on the PlantVillage database, which has images of 14 different types of plants with 38 classes. The results obtained provide a nominal error rate of 1.05% using a non-iterative approach, which signifies that the ResNet-KELM method surpasses the existing techniques in terms of accuracy and efficiency.

Ink analysis is crucial for finding ink mismatches in handwritten documents to determine their authenticity and identify potential forgery. Traditional chemical-based methods like thin layer chromatography are effective for this purpose, but they are destructive, irreversible, time-consuming, and sensitive to environmental factors. Hyperspectral document images (HSDIs) which capture the spectral information in multiple bands can reveal the material composition, thereby allowing the identification of different inks by their distinct spectral characteristics, even when the inks visually appear to be the same colour. Hyperspectral imaging thus shows great promise for forensic document analysis and authentication. Despite its potential, research in this area is still emerging. Existing HSDI-based methods show promise in detecting ink mismatches but these methods often require prior spectral information and ground truth data for the ink pixels, limiting their practical applicability. Unsupervised methods present a solution by removing the need for prior information. This work proposes a novel unsupervised ink mismatch detection method in HSDIs using deep learning-based hyperspectral analysis. The proposed method formulates ink mismatch detection as a decision problem that determines whether a deep autoencoder trained on the spectral features of specific ink pixels would be able to reconstruct the spectral features of unseen complementary ink pixels. The proposed framework works in a fully unsupervised manner, learning the spectral representations directly from data without any labeled samples or prior spectral information of the inks present. Unlike most existing methods which are supervised, the proposed unsupervised method is more practically applicable for real world forensic document analysis. Experimental results on a publicly available HSDI dataset demonstrate the superiority of the proposed method over existing methods in identifying ink mismatches in potentially fraudulent document images.