A Hybrid Multi-Model Framework for Histological Image Classification to Detect Lung Cancer

Lung cancer is one of the leading causes of cancer-related deaths worldwide, creating an urgent need for intelligent diagnostic systems that can assist pathologists in early and accurate disease detection. Traditional histopathological examination of lung tissue images relies heavily on manual analysis by medical experts, which can be time-consuming and prone to diagnostic variability when handling large volumes of medical data. Recent advancements in Artificial Intelligence (AI), Computer Vision, and Deep Learning have enabled the development of automated medical image analysis systems capable of improving the accuracy and efficiency of cancer detection. This research paper presents a Hybrid Multi-Model Framework for Histological Image Classification to Detect Lung Cancer using advanced deep learning techniques. The proposed framework integrates multiple Convolutional Neural Network (CNN) architectures such as ResNet50, DenseNet121, and Efficient Net to enhance feature extraction and classification performance. The study also explores image preprocessing, feature fusion, transfer learning, and ensemble learning approaches used in previous research works. A comparative analysis of different deep learning models highlights their strengths, limitations, and classification capabilities in histopathological image analysis. The proposed hybrid framework aims to improve classification accuracy, reduce false predictions, and support pathologists in reliable lung cancer diagnosis. The paper further emphasizes the importance of intelligent multi-model systems in modern healthcare for improving early-stage cancer detection and clinical decision-making.