Enhancing Prediction of Brain Tumor Classification Using Images and Numerical Data Features
Oumaima Saidani,
Turki Aljrees,
Muhammad Umer,
Nazik Alturki,
Amal Alshardan,
Sardar Waqar Khan,
Shtwai Alsubai,
Imran Ashraf
Affiliations
Oumaima Saidani
Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh 11671, Saudi Arabia
Turki Aljrees
Department College of Computer Science and Engineering, University of Hafr Al-Batin, Hafar Al-Batin 39524, Saudi Arabia
Muhammad Umer
Department of Computer Science & Information Technology, The Islamia University of Bahawalpur, Bahawalpur 63100, Pakistan
Nazik Alturki
Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh 11671, Saudi Arabia
Amal Alshardan
Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh 11671, Saudi Arabia
Sardar Waqar Khan
Department of Computer Science & Information Technology, The University of Lahore, Lahore 54000, Pakistan
Shtwai Alsubai
Department of Computer Science, College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia
Imran Ashraf
Department of Information and Communication Engineering, Yeungnam University, Gyeongsan 38541, Republic of Korea
Brain tumors, along with other diseases that harm the neurological system, are a significant contributor to global mortality. Early diagnosis plays a crucial role in effectively treating brain tumors. To distinguish individuals with tumors from those without, this study employs a combination of images and data-based features. In the initial phase, the image dataset is enhanced, followed by the application of a UNet transfer-learning-based model to accurately classify patients as either having tumors or being normal. In the second phase, this research utilizes 13 features in conjunction with a voting classifier. The voting classifier incorporates features extracted from deep convolutional layers and combines stochastic gradient descent with logistic regression to achieve better classification results. The reported accuracy score of 0.99 achieved by both proposed models shows its superior performance. Also, comparing results with other supervised learning algorithms and state-of-the-art models validates its performance.
