Stroke is a leading cause of morbidity and mortality worldwide, necessitating prompt and precise diagnosis to facilitate effective treatment. This thesis presents a comprehensive study on the identification and classification of stroke types using brain CT scan images through deep learning techniques. Utilizing a dataset of brain CT scans labeled as ischemic or hemorrhagic strokes, we employed advanced preprocessing steps, including normalization, noise reduction, and image enhancement, to optimize image quality. A strategic noise removal process was implemented, starting with the assessment of noise presence using Peak signal-to-noise ratio (PSNR) values. Identified noise types included Gaussian, Speckle, Poisson, Exponential, and Gamma noise. Effective noise filtering techniques such as Median and Non-Local Means filters were applied, significantly increasing PSNR values. Histogram equalization was used for further image enhancement. Additionally, rotation correction was performed using the midsagittal principle to ensure the correct orientation of images. We trained and fine-tuned several deep-learning models, including EfficientNetB0. The models' performances were remarkable, with EfficientNetB0 achieving an accuracy of 100% and perfect precision, recall, and F1-score, demonstrating its superior classification capability. Overall, this study underscores the effectiveness of advanced preprocessing techniques and parameter tuning in enhancing the performance of deep learning models for stroke classification, ultimately improving diagnostic accuracy and patient outcomes. This study's limited sample size and lack of MRI data constrain its ability to fully analyze stroke characteristics and outcomes, affecting the generalizability of the findings. Future research should include a larger, more diverse cohort and incorporate MRI data to enhance the robustness and applicability of the results. Keywords: Stroke Disease, Medical imaging, Brain CT scans, Transfer learning, Stroke Disease Identification.