Automatic and accurate auxiliary detection of lung cancer pathological classification based on novel lightweight deep learning model - research findings

Sep 22, 2026

Lung cancer remains one of the leading cancers worldwide, and rapid, accurate pathological subtype detection is essential for effective treatment planning and patient management. Pathology-based classification currently depends heavily on clinical experts, creating time, workload, and subjectivity constraints that can limit diagnostic speed and precision. This study proposes BreezeNet, a lightweight deep learning model for automated identification of lung adenocarcinoma, lung squamous cell carcinoma, and benign lung tissue, showing strong performance with substantially fewer parameters.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages18

Reading Time45 min

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Abstract

Background

Methods

Results

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Keywords

Introduction

Wang et al. Discover Oncology (2026) 17:325 [https://doi.org/10.1007/s12672-026-04487-2](https://doi.org/10.1007/s12672-026-04487-2) Discover Oncology ANALYSIS Open Access Automatic and accurate auxiliary detection of lung cancer pathological classification based on novel lightweight deep learning model Shidong Wang1*, FaTian2 andYupeng Niu3 *Correspondence: Shidong Wang [yjht16@126.com](yjht16@126.com) 1Department of Respiratory Medicine, Shaoxing Second Hospital, Shaoxing 312000, China 2College of Information Engineering, Sichuan Agricultural University, Ya’an 625000, China 3Tianjin Key Laboratory of Radiation Medicine and Molecular Nuclear Medicine, Institute of Radiation Medicine, Chinese Academy of Medical Science & Peking Union Medical College, Tianjin 300192, China Abstract Background Lung cancer is one of the major cancers worldwide, and rapid, accurate diagnosis is crucial for subsequent treatment and management. Currently, pathological subtype detection requires clinical experts to invest significant time and effort, making the development of automatic, efficient detection models essential. Methods This study developed a novel deep learning model named BreezeNet for the recognition of lung adenocarcinoma, lung squamous cell carcinoma, and benign lung tissue. BreezeNet is a lightweight deep learning framework specifically designed for precise and automated diagnosis of lung adenocarcinoma, lung squamous cell carcinoma, and benign lung tissue. Compared with current mainstream deep learning models such as VGG, GoogleNet, and MobileNet, BreezeNet demonstrated superior performance in key metrics such as precision and accuracy. Results In our study, we developed a lightweight deep learning model named BreezeNet for the automatic classification of lung cancer cells. The experimental results show that BreezeNet performs excellently across various metrics, particularly in terms of the number of parameters. Specifically, BreezeNet achieved a precision of 0. 9749, a recall of 0 . 9742, an F1-score of 0 . 9742, and an accuracy of 0 . 9789, which are slightly better than traditional deep learning models such as AlexNet, VGG, GoogleNet, ResNet, and MobileNet. However, the most significant advantage of BreezeNet lies in its parameter count, which is only 1,256,679, far lower than AlexNet’s 14,587,587 and ResNet’s 23,514,179 . This means that our model is not only competitive in terms of performance but also significantly reduces the computational resource requirements, greatly enhancing the model’s lightweight nature and deployment efficiency. Conclusion Compared with traditional deep learning models such as AlexNet, VGG, and ResNet, BreezeNet achieves slightly better performance across all key metrics, with up to 1. 6% higher accuracy, 1. 76% higher F1-score, and over 18× fewer parameters, highlighting its superior lightweight design and diagnostic effectiveness. Our developed deep learning model can efficiently perform automated subtyping of lung cancer cells, providing accurate diagnostic recommendations for doctors. This will help improve the efficiency of lung cancer diagnosis, thereby enhancing patient survival rates. © The Author(s) 2026. Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and yo

FAQs

What problem does the study address in lung cancer diagnosis?

It targets the need for rapid, accurate pathological subtype detection, which currently requires clinical experts and involves significant time, workload, and subjectivity.

What is BreezeNet and what does it classify?

BreezeNet is a lightweight deep learning model designed to recognize lung adenocarcinoma, lung squamous cell carcinoma, and benign lung tissue for automated subtyping.

How does BreezeNet compare with mainstream deep learning models?

It shows slightly better performance on key metrics such as precision, recall, F1-score, and accuracy, while using far fewer parameters than models like AlexNet, VGG, GoogleNet, ResNet, and MobileNet.

Sep 22, 2026

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