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Updated: Sep 12, 2025

Unilateral Lung Volume Analysis Using Micro-CT for Enhanced Assessment of Pulmonary Fibrosis in Preclinical Models

Unilateral Lung Volume Analysis Using Micro-CT for Enhanced Assessment of Pulmonary Fibrosis in Preclinical Models

Published on: June 20, 2025

Enhanced hyper tuning using bioinspired-based deep learning model for accurate lung cancer detection and

Jyoti Kumari¹, Sapna Sinha¹, Laxman Singh²

¹Department of Computer Science and Engineering, Amity Institute of Information Technology, Amity University, Noida, Uttar Pradesh, India.

The International Journal of Artificial Organs

|August 9, 2025

Summary

This study introduces an Enhanced Hyper Tuning Deep Learning (EHTDL) model for accurate lung cancer (LC) detection. The novel approach significantly improves early diagnosis and classification efficiency in CT images.

Keywords:

Bioinspired algorithms Early diagnosis Fractal Edge Classifier Grey Wolf optimization differential evolution gray level co-occurrence matrix based texture analysis mask R-CNN medical imaging smooth edge enhancement

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Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

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Published on: April 14, 2023

Area of Science:

Medical Imaging
Artificial Intelligence
Oncology

Background:

Lung cancer (LC) remains a leading cause of cancer mortality globally, underscoring the need for effective early detection methods.
Current LC detection techniques are hampered by computational complexity, data integration challenges, scalability issues, and clinical validation difficulties.

Purpose of the Study:

To develop an Enhanced Hyper Tuning Deep Learning (EHTDL) model for improved accuracy and efficiency in lung cancer detection and classification.
To address limitations of existing methods using bioinspired algorithms and advanced deep learning techniques.

Main Methods:

Preprocessing CT images using Smooth Edge Enhancement (SEE) and GLCM-based Texture Analysis for feature extraction.
Employing a Hybrid Feature Selection approach with Grey Wolf optimization (GWO) and Differential Evolution (DE) for feature refinement and dimensionality reduction.
Utilizing Mask R-CNN for precise lung segmentation and a Deep Fractal Edge Classifier (DFEC) with fractal blocks for LC characteristic learning.

Main Results:

The EHTDL model achieved high performance metrics: 99% accuracy, 100% precision, 98% recall, and 99% F1-score.
Demonstrated robustness and effectiveness in LC detection and classification.
Indicated suitability for real-time clinical applications due to its scalability and efficiency.

Conclusions:

The proposed EHTDL model offers a promising solution for early lung cancer detection, significantly enhancing patient care.
The model's advanced deep learning architecture and optimization techniques overcome existing challenges in LC diagnosis.
This research paves the way for more efficient and accurate clinical tools in the fight against lung cancer.