The rib cage fracture is broadly categorized in to two main groups; those are external case and internal case. The external case occur by direct blow or crash like motor vehicle, car accident, falling down, and other activities that cause crash injury. The internal cases are coughing or metastatic cancer also results in rib cage fractures. The health sector service has not sufficient and skilled man power in addition to modern technological devices. This diagnosing technique using X-ray image may be exposed to clinical reading error because of X-ray reading expert shortage and human vision variation. The general objective of this research is to investigate the rib-cage fractures in chest using radiographic image processing to reduce radiographic reading error problems in clinical investigation. Methods: The proposed methodology for Detection of Rib Cage Fracture Processing mainly concerned on Image processing procedural performance and actions. The RibCage Fractures research study focused on the following main aspects methodological system those are: data collection, data processing techniques and Image training and testing. In order to achieve the objective of the research the radiographic image processing techniques such as datasets, Image augmentation, Image Preprocessing, and Image Segmentation techniques are applied by using MATLAB tool. The raw data of radiography rib-cage image was collected from KIDANEMIRET clinic. A special focus was given to identify each patent radiography image result whether it is normal or abnormal based on the doctor diagnose result and written in individual patent card. The total image taken from the above clinic is 500 and the type of radiography images is Jpg type. From the classification and training model processing we evaluated and achieved 98.00%display accuracy, the elapsed time is 8 minute and 19 second and learning rate is 1.0000 e 4 . Key words: Radiography Image, CNN, Rib-Cage Fractures, de-noising, Feature extraction and ANN.