The format and quality of the input data are critical when using magnetic resonance imaging (MRI) to identify brain cancers. This study examines three widely used image formats—JPEG (.jpg), PNG (.png), and NIfTI (.nii)—to determine their suitability for deep learning-based diagnosis. Because NIfTI allows for substantial information and highly precise 3D volumetric data, it is ideal for preserving diagnostic data. While JPEG’s lossy compression generates artifacts that could impair model performance, PNG delivers lossless compression appropriate for 2D slice-based preprocessing and display. Despite having a bigger file size and load time, NIfTI offers the fastest denoising time and the lowest mean squared error (MSE), according to experimental results backed by literature. JPEG, on the other hand, loses the most data, whereas PNG strikes a compromise between quality and In order to assess the viability of three popular picture formats for deep learning-based diagnosis, this study compares them: JPEG (.jpg), PNG (.png), and NIfTI (.nii). NIfTI is perfect for retaining diagnostic data because it enables 3D volumetric data with high precision and extensive metadata. PNG offers lossless compression suitable for 2D slice-based preprocessing and presentation, but JPEG’s lossy compression creates artifacts that may affect model performance.