Clinical validation of lightweight CNN architectures for reliable multi-class classification of lung cancer using histopathological imaging techniques
Ali Raza; Fareeha Hanif; Heba Abdelgader Mohammed · 2026 · Scientific Reports
WASTE classifies this as Negative / Null Result Report · AI classification, approximate
The study found no significant effect — useful as a negative control or null benchmark for your own design.
Abstract
Lung cancer remains one of the leading causes of cancer-related mortality worldwide, and accurate early diagnosis plays a critical role in improving patient survival. In this study, a comparative analysis of multiple lightweight Convolutional Neural Network (CNN) variants is presented for multi-class lung cancer classification using histopathological images. Four CNN architectures were designed to systematically explore the trade-off between model complexity and classification performance. Each variant was trained and evaluated within a unified experimental framework incorporating data augment
Abstract by Ali Raza; Fareeha Hanif; Heba Abdelgader Mohammed, Scientific Reports (2026) — licensed CC BY 4.0.
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Metadata source: OpenAlex · DOI 10.1038/s41598-026-36652-6