Vijaya Kishore Veparala

Multimodal medical image fusion combines complementary anatomical and functional information from different imaging modalities into a single image to support clearer visualization and more informed clinical interpretation. Conventional fusion methods may introduce blurring, reduce contrast, or lose important edge and texture information, particularly when curved anatomical boundaries are not represented effectively. This study proposes a Bendlet-based medical image fusion framework that integrates the Bendlet Transform with Contrast Visibility (CV) and Modified Spatial Frequency (MSF) fusion rules. The source images are first decomposed into low- and high-frequency subbands using four multiscale levels and directional subbands of [8, 8, 16, 16]. A bending parameter of 0.5 is used to represent curved image structures, and the transformed coefficients are processed in non-overlapping 32 × 32 blocks. The low-frequency coefficients are fused using the CV rule to preserve intensity and contrast information, whereas the high-frequency coefficients are fused using the MSF criterion to retain edges, textures, and local structural details. The final fused image is reconstructed through the inverse Bendlet Transform. The proposed method is evaluated on five medical-image databases using qualitative inspection and objective fusion metrics, including entropy, standard deviation, mutual information, spatial frequency, fusion symmetry, structural similarity, and edge-based quality measures. The reported comparisons indicate that the proposed method preserves complementary information and produces visually clearer fused images than the comparison techniques considered in the study. These findings suggest that Bendlet-based decomposition combined with frequency-specific fusion rules is a promising approach for multimodal medical image fusion, although broader clinical validation and statistical analysis are required before diagnostic use.

Lung and colon cancers are among the most common cancer in all over the world, affecting both males and females. Early detection is crucial to reduce mortality. However, the difficulty of diagnosis often depends on the histopathologist’s experience. Histopathological images offer detailed tissue-level information, but manual analysis is time-consuming and labor-intensive. Variability within classes and inconsistent staining make diagnosis challenging. Recently, Deep Learning (DL) has attained popularity for improving medical image interpretation. Therefore, a new module for Lung and Colon Cancer detection from histopathological images named Harmonic Tanimoto constrained Huber loss (HTCH-Loss) with Correlative Capsule Scale Attention Network (CCSAN) framework is designed to tackle these problems. The histopathological images based on minority and majority classes are initially fed to the Dual-Generative Adversarial Network (GAN) mechanism for realistic synthetic image generation. Here, both real and synthetic images are obtained. After that, the histopathological image is integrated to form a new dataset. Then, blood cell segmentation is done by Lightweight Feature Attention Network (LFANet). Thereafter, Deep Ensemble Feature Extraction (Deep EFE) is performed. Afterward, lung cancer detection is done using CCSAN with HTCH-Loss. Moreover, CCSAN is modified by using HTCH-Loss, which is the integration of Tanimoto loss, constrained focal loss and Huber loss, with the harmonic concept. Simultaneously, a similar process is followed for colon cancer detection. Experimental results demonstrate that the proposed CCSAN with HTCH-Loss attains superior accuracy values of 95.237% for lung cancer detection and 96.669% for colon cancer detection.

This work presents a novel technique to develop the three-valued logic (TVL) circuit schematics for very large-scale integration (VLSI) applications. The TVL is better alternative technology over the two-valued logic because it provides decreased interconnect connections, fast computation speed and decreases the chip complexity. The TVL based complicated designs such as half-adder and multiplier circuits are designed utilizing the Pseudo N-type carbon nanotube field effect transistors (CNTFETs). The proposed TVL half adder multiplier schematics are developed in HSPICE tool. Additionally, the delay and circuit area for the half- adder and multiplier circuits are investigated and compared to the complementary circuits. The memory usage and CPU time for the proposed circuits are also analyzed. It is observed that the proposed circuit designs show the improved performance up to 43.03% on an average over the complementary designs

In the present age of information technology, information is the most important factor in determining how different paradigms will progress. This information needs to be mined out of a massive computer treasure trove. The rise in the amount of data been analyzed and interpreted is a direct result of the proliferation of more powerful processing platforms, the increase in the amount of storage space available, and the transition toward the use of electronic platforms. A thorough study of Big Data, its characteristics, and the role that Subspace clustering algorithm plays is described in this work. The most important contribution that this paper makes is that it reads a lot of previous research and then makes a thorough presentation about the different ways that other authors have classified subspace clustering methods. In addition, significant algorithms that are capable of acting as a benchmark for any future development have been provided with a short explanation