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Deep Learning Models for Classification of Brain Tumor with Magnetic Resonance Imaging Images Dataset

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Computational Intelligence in Oncology

Part of the book series: Studies in Computational Intelligence ((SCI,volume 1016))

Abstract

Brain tumor is known among the most aggressive diseases among adults and children around the world. It is estimated that every year, more than 11,700 people around the world are being diagnosed with the disease. Moreover, brain tumor has also been among the most common cancers affecting people in Nigeria. In fact, in the year 2018, according to WHO, Nigeria has 2.1% incidence cases and a 2.7% mortality rate, respectively. Consequently, many ways and techniques are being used for classifying and detecting brain tumors; however, the most common non-invasive technique is called MRI. Nowadays, many MRI images dataset of brain tumors is being generated and examined by radiologists for the classification of the brain tumor as either benign or malignant. Nevertheless, the manual examination and classification of the MRI images dataset are prone to errors due to complexities and uncertainty that are often associated with the examination and classification, respectively. Therefore, a deep learning technique, which is an applied artificial technique, is used to develop a system or model that trains itself automatically or learns sensible features from the dataset. Hence, this technique has been harnessed in this work to develop the classification models of the brain tumors with MRI images dataset. VGG16 and ResNet50 deep learning algorithms were used in developing the models, and each model was evaluated based on accuracy, sensitivity, specificity, and area under the ROC curve evaluations metrics. The VGG16 classification model has comparatively outperformed the ResNet50 model for being able to classify the brain tumor as either benign or malignant with 96% accuracy and for the ability to correctly classify malignant cases with 94.30% accuracy. However, for the ability to correctly classify benign cases of brain tumors, ResNet50 has comparatively outperformed the VGG16 classification model with 93.10% accuracy. Yet, in terms of how much the model differentiates between malignant cases against benign cases, the model developed with the ResNet50 deep learning algorithm comparatively outperformed the VGG16 classification model with 91.20% accuracy. Therefore, the VGG16 classification model which has the highest sensitivity of 94.30% could be used for the classification of the brain tumor and could also be used as an adjuvant tool in radiology departments in various hospitals in Nigeria.

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Authors and Affiliations

  1. Computer Science Department, Federal University of Kashere, Gombe, Nigeria

    L. J. Muhammad & O. S. Dada

  2. Libyan Academy, Misrata, Libya

    Ibrahim Badi

  3. College of Computer Science & Engineering, University of Hafr Al Batin, Al Jamiah, Hafar Al Batin, 39524, Saudi Arabia

    Ahmed Abba Haruna

  4. Department of Computer Science, Yobe State University, Damaturu, Yobe, Nigeria

    I. A. Mohammed

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  1. L. J. Muhammad
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  2. Ibrahim Badi
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  3. Ahmed Abba Haruna
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  4. I. A. Mohammed
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  5. O. S. Dada
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Correspondence to L. J. Muhammad .

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  1. Department of Computer Science, Jamia Millia Islamia, New Delhi, India

    Khalid Raza

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Muhammad, L.J., Badi, I., Haruna, A.A., Mohammed, I.A., Dada, O.S. (2022). Deep Learning Models for Classification of Brain Tumor with Magnetic Resonance Imaging Images Dataset. In: Raza, K. (eds) Computational Intelligence in Oncology. Studies in Computational Intelligence, vol 1016. Springer, Singapore. https://doi.org/10.1007/978-981-16-9221-5_9

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