we have developed a deep learning model using a Convolutional Neural Network (CNN) architecture implemented with TensorFlow and Keras. This CNN is specifically tailored for the analysis of medical images. The model has undergone extensive training using a diverse dataset of labelled images, enabling it to classify and identify different conditions. For predictions, we utilize the well-established algorithm of the CNN to process and interpret input mri images. The model has demonstrated a commendable accuracy. This high accuracy probability suggests a reliable identification of abnormalities during our evaluation phase. Additionally, the model's precision, recall, and F1 score showcase its effectiveness in minimizing false positives and false negatives.
The model was trained for 20 epochs, and during the final epoch, it achieved an impressive accuracy of 99.8% on the validation set. Additionally, when evaluated on the independent test set, the model showed a high accuracy of 99.22%. The loss values are also notably low. These results indicate that the model has performed exceptionally well and demonstrates strong generalization capabilities.
we have developed a deep learning model using a Convolutional Neural Network (CNN) architecture implemented with TensorFlow and Keras. This CNN is specifically tailored for the analysis of medical images. The model has undergone extensive training using a diverse dataset of labelled images, enabling it to classify and identify different conditions. For predictions, we utilize the well-established algorithm of the CNN to process and interpret input mri images. The model has demonstrated a commendable accuracy. This high accuracy probability suggests a reliable identification of abnormalities during our evaluation phase. Additionally, the model's precision, recall, and F1 score showcase its effectiveness in minimizing false positives and false negatives.
The model was trained for 20 epochs, and during the final epoch, it achieved an impressive accuracy of 99.8% on the validation set. Additionally, when evaluated on the independent test set, the model showed a high accuracy of 99.22%. The loss values are also notably low. These results indicate that the model has performed exceptionally well and demonstrates strong generalization capabilities.