Advancing brain tumor detection and classification in Low-Dose CT images using the innovative multi-layered deep neural network model

Background Effective brain tumour therapy and better patient outcomes depend on early tumour diagnosis. Accurate diagnosis can be hampered by traditional imaging techniques’ frequent struggles with low resolution and noise, especially in Low Dose CT scans. Objectives: Through the integration of deep learning methods and sophisticated image processing techniques, this study seeks to establish a novel framework, the Multi Layered Chroma Edge Deep Net (MLCED-Net), to improve the accuracy of brain tumour diagnosis in Low Dose CT images. Methods Using the Lucy-Richardson technique for picture deblurring, Adaptive Histogram Equalisation, and pixel normalization to lower noise and enhance image quality are some of the pre-processing stages that are part of the suggested strategy. Main characteristics from the processed pictures are then retrieved, including mean, energy, contrast, and entropy. Following the feeding of these characteristics, the MLCED-Net model is used for classification and segmentation tasks. It utilises a 15-layer deep learning architecture. Results The MLCED-Net model outperformed previous techniques by achieving an amazing accuracy rate of 98.9% in the detection of brain tumours. The suggested procedures were effective, as seen by the significant increases in image quality that the Peak Signal-to-Noise Ratio (PSNR) values showed after post-processing. Conclusions: A reliable method for brain tumour diagnosis in low-dose CT scans is offered by the MLCED-Net framework's combination of multi-layered autoencoders, color-based operations, and edge detection techniques. The present work underscores the capacity of sophisticated deep learning models to augment diagnostic precision, hence augmenting patient care and results.