Deep Hybrid Neural Network: Unveiling Lung Cancer With Deep Hybrid Intelligence from CT Scans

Lung cancer remains one of the main causes of cancer-related mortality globally, often diagnosed at late stages due to non-specific early symptoms. Traditional diagnostic methods, including biomarker analysis, biopsies, and image analysis, have long evaluation periods, subjectivity, and interobserver variability. To improve diagnostic accuracy and efficiency, this study introduces a deep hybrid neural network (DHNN) for lung cancer diagnosis from histopathology images. The proposed model incorporates EfficientNet as the basis architecture and utilizes data augmentation techniques to optimize performance. The suggested DHNN outperformed the traditional convolutional neural network (94.03%) by achieving an accuracy of 98.86%. It demonstrated exceptional precision, identifying malignant patients with higher reliability while reducing misclassification rates. This strategy provides a strong and effective substitute for current techniques, speeding up diagnosis and maybe enhancing patient outcomes.