Abstract: Cardiovascular Diseases are the number one cause for deaths in the whole world and heart attack or Myocardial Infarction (MI) reserves the biggest share in this rate. MI is defined as a block in the heart coronary artery that causes deprivation from oxygen to the heart muscles and could lead to the death of that muscle or even the death of the patient. Currently MI is first diagnosed by recording ECG monitor for the patient with chest pain then the interpretation of the results is done manually by the cardiologist, but this process is very hard and time consuming and threatens lives if not treated as fast as possible. With the revolution of machine learning and deep learning it was needed to ease the pressure on the doctors and help them by computerizing this process to provide the diagnoses in short and reliable manner. Many studies and models were implemented for this purpose and most of them employed Convolution Neural Network for its strong ability to extract features and learn them from high dimension datasets such as images and 1-dimension signals or waves. Few research studied the use of image-based ECG to detect MI. In this study an effective deep learning neural network was used to classify ECG images into MI and normal cases. Simple network structure was used for the mentioned purpose and the fully connected layer was omitted due to the issues related to its usage and the original image size was used with 12 leads ECG. The model was suffering from overfitting, therefor a combination of optimization techniques was used to reduce it and get good performance. The data was fully augmented and oversampled as well to reduce the overfitting problem. Physikalisch-tachnische bundesanstalt (PTB) Diagnostic ECG database was used for the model experiments. the Area Under the Curve was used as the performamve ovaluation metrics. The rosults obtained wore promising, the model achioved 99% AUC in training, 96% in validation and 94% in testing predictions where the model could datect MI with 98% comactly classified. It was concluded that the model could detect MI effectively and data augmentation with oversampling had the main impact on the performance.