Volume V | Issue 4 | 2026 | Paper ID: IJATEM-V05I04P1

Densenet-Based Deep Learning Architecture for Multi-Subtype Tracheal Tumor Detection

Author(s)

T. Brinda, Srikanth Mylapalli

Cite This

Brinda T., "Densenet-Based Deep Learning Architecture for Multi-Subtype Tracheal Tumor Detection", International Journal of Advanced Trends in Engineering and Management, vol. 05, no. 04, pp. 1-14, 2026.

Abstract

This research presents a DenseNet-based deep learning architecture for detecting multi-subtype tracheal tumors that employs an efficient medical image processing pipeline. The proposed method begins with data collection and progresses through preprocessing processes such as image resizing, contrast enhancement with adaptive gamma correction, noise reduction, and bilateral filtering to improve image quality. The improved images are then segmented using the watershed technique, which efficiently separates tumor locations from surrounding tissues. Critical textural information are retrieved using the Local Binary Pattern (LBP) approach and then classed using the DenseNet model to accurately identify tumor subtypes. The proposed system performs highly with an accuracy of 94.1%, precision of 94.2%, recall of 94.1%, and F1 score of 94.1%, indicating robustness and reliability. The combination of these steps results high accuracy, efficiency, and stability in detecting and categorizing tracheal cancers, allowing for better clinical diagnosis and decision-making.

Keywords

DenseNet, Bilateral filter, Water Shed Segmentation, Local Binary Pattern (LBP).

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