In the health field, several thousand images are generated every day in medical imaging establishments. On the one hand, the volume of information involved is still far from being fully controlled. On the other hand, the development of machine learning tools today opens the way to a new generation of image analysis in this context of “BigData”. Moreover, our approach is a part towards this dynamic research using Gabor Filter Algorithm in image processing, analysis and diagnosis. In order to test the robustness of our algorithm and its degree of application in the context of BigData, we tested, in a first analysis phase, our algorithm on an image-database containing 320 mammograms. The precision obtained is estimated at 75 percent for a recall of 33 percent. In a second analysis phase, we performed the test on an image data-base containing 1000 medical images. The precision obtained is estimated at nearest 70 percent for a recall of 33 percent. Although the precision obtained in this first step is far from perfect, our processing algorithm remains promising and shows a good adaptation in contest of “Big Data”. The purpose of this research work is to contribute to machine learning. Therefore, the analysis process based on Gabor Filter can distinguish, more and concisely, tumor mammograms from healthy mammograms in Big Data context.