Implementing Deep Learning Models for Early Detection and Segmentation of Lung Cancer from Medical Imaging

Abstract

Once surprisingly, lung cancer still ranks among the top causes of cancer deaths globally, early stage diagnosis is the key to increasing the survival benefits of the patient. Lung cancer diagnosis using classical methods like manual image analysis and histogram analysis is time consuming and has high possibilities of human errors. Lung cancer detection and segmentation with deep learning are promising, but they still encountered challenges of high accuracy in noisy or low-quality medical images. This study proposes an advanced deep learning framework articulated by CNNs with the integration of data augmentation methods and multiple scale segmentation for the automated detection and segmentation of lung cancer, on a dataset of CT scans. More importantly, when compared with existing techniques, it has a much higher accuracy in detecting the tumour areas, even under situations of varying quality of the images. Experimental data shows enhanced sensitivity and specificity over conventional strategies. This proposed model not only shortens the diagnostic time but it also provides uniform, trustful results which reduce the misdiagnosis occurrence. We are making strides that further advance AI tools to enhance clinical practice and we hope may improve the early detection of lung cancer and save lives.