EfficientNetB3-Based Lung Cancer Classification from CT Scan Images: A Deep Learning Approach

In the field of medical diagnostics, lung cancer disease identification is a vital task. Deploying deep learning technology with EfficientNetB3 architecture as its powerhouse seeks to raise the accuracy and reliability of lung cancer classification in this study. The dataset consists of 1,187 high-resolution images showing lung cancer pathology in a variety of forms. Using the EfficientNetB3 model, 40 iterations of training have been carried out, batch size 20, and learning rate 0.001 are used. This enables the model to pick up on fractional changes that are symptomatic of different kinds of lung cancers. Model training is an arbitrary manipulation of model parameters designed to fine-tune them. The evaluation method for model performance is based on its capacity to recognize CT scan images like that from lung cancer correctly. The proposed EfficientNetB3 model, achieving an accuracy rate of 93.33%, proves remarkably successful. This result indicates the model's proficiency in distinguishing between different types of lung cancer. This enables more accurate diagnosis and treatment planning. And the findings of this recalibrate routes through which patient care is delivered. It hopes to replace conventional medical models with more efficient ones by providing diagnosis capabilities for forms of lung cancer that are both accurate and timely. Thus, misdiagnosis will decrease and targeted treatments can be pursued more effectively. Plus, the research's success illustrates the feasibility of deep learning strategies particularly EfficientNetB3 architecture for promoting medical imaging analysis and health care provision.