At present, the prediction of brain tumors is performed using Machine Learning (ML) and Deep Learning (DL) algorithms. Although various ML and DL algorithms are adapted to predict brain tumors to some range, some concerns still need enhancement, particularly accuracy, sensitivity, false positive and false negative, to improve the brain tumor prediction system symmetrically. Therefore, this work proposed an Extended Deep Learning Algorithm (EDLA) to measure performance parameters such as accuracy, sensitivity, and false positive and false negative rates. In addition, these iterated measures were analyzed by comparing the EDLA method with the Convolutional Neural Network (CNN) way further using the SPSS tool, and respective graphical illustrations were shown. The results were that the mean performance measures for the proposed EDLA algorithm were calculated, and those measured were accuracy (97.665%), sensitivity (97.939%), false positive (3.012%), and false negative (3.182%) for ten iterations. Whereas in the case of the CNN, the algorithm means accuracy gained was 94.287%, mean sensitivity 95.612%, mean false positive 5.328%, and mean false negative 4.756%. These results show that the proposed EDLA method has outperformed existing algorithms, including CNN, and ensures symmetrically improved parameters. Thus EDLA algorithm introduces novelty concerning its performance and particular activation function. This proposed method will be utilized effectively in brain tumor detection in a precise and accurate manner. This algorithm would apply to brain tumor diagnosis and be involved in various medical diagnoses after modification. If the quantity of dataset records is enormous, then the method’s computation power has to be updated. © 2024 Elsevier B.V., All rights reserved.