Detecting and classifying a brain tumor is a challenge that consumes a radiologist's time and effort while requiring professional expertise. To resolve this, deep learning techniques can be used to help automate the process. The aim of this paper is to enhance the accuracy of brain tumor classification using a new layered architecture of deep neural networks rather than the current state-of-the-art algorithms. In this paper, we propose automated tumor classification by concatenating two convolutional neural network structures of layers and tuning the hyperparameters by utilizing Bayesian optimization. The proposed solution focuses on enhancing the accuracy of classifying tumors to increase the level of trust in the technologies employed in the medical field. The work is tested and evaluated to predict the classification of magnetic resonance imaging inputs and achieving a higher accuracy (97.37%) than other similar works, with accuracies between 84.19% and 96.13%, for the same dataset.

Improved disease prediction accuracy and reliability are the main concerns in the development of models for the medical field. This study examined methods for increasing classification accuracy and proposed a precise and reliable framework for categorizing breast cancers using mammography scans. Concatenated Convolutional Neural Networks (CNN) were developed based on three models: Two by transfer learning and one entirely from scratch. Misclassification of lesions from mammography images can also be reduced using this approach. Bayesian optimization performs hyperparameter tuning of the layers, and data augmentation will refine the model by using more training samples. Analysis of the model’s accuracy revealed that it can accurately predict disease with 97.26% accuracy in binary cases and 99.13% accuracy in multi-classification cases. These findings are in contrast with recent studies on the same issue using the same dataset and demonstrated a 16% increase in multi-classification accuracy. In addition, an accuracy improvement of 6.4% was achieved after hyperparameter modification and augmentation. Thus, the model tested in this study was deemed superior to those presented in the extant literature. Hence, the concatenation of three different CNNs from scratch and transfer learning allows the extraction of distinct and significant features without leaving them out, enabling the model to make exact diagnoses.