The aim of this study is to the most prevalent and dangerous disease, brain tumors, which have very low life expectancies in their most advanced stages. Therefore, treatment planning is an essential stage in improving patients' quality of life. Several imaging techniques, including computed tomography (CT), magnetic resonance imaging (MRI), and ultrasound images, are frequently used to evaluate the tumor in a brain. In order to generate probability maps of tumor presence in brain images, the suggested method first conducts brain tumor segmentation using a modified Dense-Net architecture that includes skip connections and a softmax layer. The retrieved features from the brain scans are then used to classify them into those with Alzheimer's disease, Parkinson's disease, or normal brain function using a completely connected layer and softmax activation function. the split-up images. Brain tumor segmentation and classification are significant issues in medical image analysis that can aid in the detection and treatment of brain tumors. These results show that the proposed technique for extracting Dense-Net features, which is then fed into a CNN for segmentation and classification and disease detection, may improve the efficacy and precision of medical diagnosis and therapy and may provide new insights into the mechanisms underlying neurodegenerative illness. The findings of the experiments show that the suggested method for classifying and segmenting brain tumors is effective. The proposed deep learning model was compared to transfer learning methods already in use on the same MRI dataset, and while classification results improved, processing times were reduced by using Dense-Net features and CNN architecture to generate accurate segmentation and classification results. This method has the potential to be used in clinical situations for the detection and treatment of tumors in the brain.

Keywords: Parkinson’s Disease, Alzheimer’s Disease, Segmentation, Classification, Softmax layer, DenseNet architecture, CNN.

[1] Zacharaki E.I. et al. (2009). Classification of brain tumor type and grade using MRI texture and shape in a machine learning scheme. J. Int. Soc. Magn. Reson. Med., 62(6): 1609–1618.

[2] Litjens G. et al. (2017). A survey on deep learning in medical image analysis. Med. Image Anal., 42: 60–88.

[3] Liu D., Liu Y., & Dong L. (2019). G-ResNet: Improved ResNet for brain tumor classification. In Proc. Int. Conf. Neural Inf. Process., Cham, Switzerland: Springer, Pages 535–545.

[4] Zhou L., Zhang Z., Chen Y.C., Zhao Z.Y., Yin X.D., & Jiang H.B. (2019). A deep learning-based radiomics model for differentiating benign and malignant renal tumors. Transl. Oncol., 12(2): 292–300.

[5] Hosny K.M., Kassem M.A., & Foaud M.M. (2018). Skin cancer classification using deep learning and transfer learning. In Proc. 9th Cairo Int. Biomed. Eng. Conf., Pages 90–93.

[6] Rodrigues D.D.A., Ivo R.F., Satapathy S.C., Wang S., Hemanth J., & Reboucas F. (2020). A new approach for classification skin lesion based on transfer learning, deep learning, and IoT system. Pattern Rec. Lett., 136: 8–15.

[7] Yadav S.S., & Jadhav S.M. (2019). Deep convolutional neural network based medical image classification for disease diagnosis. J. Big Data, 6(1): 1–18.

[8] Li X., Pang T., Xiong B., Liu W., Liang P., & Wang T. (2017). Convolutional neural networks based transfer learning for diabetic retinopathy fundus image classification. In Proc. 10th Int. Congr. Image Signal Process., Biomed. Eng. Informat., Pages 1–11.

[9] Ahmed K.B., Hall L.O., Goldgof D.B., Liu R., & Gatenby R.A. (2017). Fine-tuning convolutional deep features for MRI based brain tumor classification. In Proc. Med. Imag., Comput.-Aided Diagnosis, Art. no. 101342E.

[10] Cheng J. et al. (2016). Retrieval of brain tumors by adaptive spatial pooling and fisher vector representation. PLoS One, 11(6): e0157112.

[11] Ismael M.R., & Abdel-Qader I. (2018). Brain tumor classification via statistical features and back-propagation neural network. In Proc. IEEE Int. Conf. Electron./Inf. Technol., Pages 0252–0257.

[12] Abiwinanda N., Hanif M., Hesaputra S.T., Handayani A., & Mengko T.R. (2018). Brain tumor classification using convolutional neural network. In World Congress on Medical Physics and Biomedical Engineering, Singapore: Springer, Pages 183–189.

[13] Pashaei A., Sajedi H., & Jazayeri N. (2018). Brain tumor classification via convolutional neural network and extreme learning machines. In Proc. 8th Int. Conf. Comput. Knowl. Eng., Pages 314–319.

[14] Afshar P., Mohammadi A., & Plataniotis K.N. (2018). Brain tumor type classification via capsule networks. In Proc. 25th IEEE Int. Conf. Image Process., Pages 3129–3133.

[15] Afshar P., Plataniotis K.N., & Mohammadi A. (2019). Capsule networks for brain tumor classification based on MRI images and coarse tumor boundaries. In Proc. Int. Conf. Acoust., Speech Sig Process., Pages 1368–1372.

[16] Widhiarso W., Yohannes Y., & Prakarsah C. (2018). Brain tumor classification using gray level co-occurrence matrix and convolutional neural network. Indonesian J. Electron. Instrum. Syst., 8(2): 179–190.

[17] Bhardwaj A., Di W., & Wei J. (2018). Deep Learning Essentials: Your Hands On Guide to the Fundamentals of Deep Learning and Neural Network Modeling. Packt Publishing Ltd.

[18] Lo S.C.B., Li H., Wang Y., Kinnard L., & Freedman M.T. (2002). A multiple circular path convolution neural network system for detection of mammographic masses. IEEE Trans. Med. Imag., 21(2): 150–158.

[19] Ur Rehman S. et al. (2019). Unsupervised pre-trained filter learning approach for efficient convolution neural network. Neurocomputing, 365: 171–190.

[20] Lo S.C.B., Chan H.P., Lin J S., Li H., Freedman M.T., & Mun S.K. (1995). Artificial convolution neural network for medical image pattern recognition. Neural Netw., 8(7/8): 1201–1214.

Source of Funding:

This study did not receive any grant from funding agencies in the public or not-for-profit sectors.

Competing Interests Statement:

The authors have declared no competing interests.

Consent for Publication:

The authors declare that they consented to the publication of this research work.

Ethical Approval:

Not Applicable.

Author’s Contribution:

Both the authors took part in data collection and manuscript writing equally.