Arabahmadi M., Farahbakhsh R., Rezazadeh J. (2022): Deep learning for smart healthcare - A survey on brain tumor detection from medical imaging. Sensors 22, 1960.
Cao J., Yan M., Jia Y., Tian X., Zhang Z. (2021): Application of a modified inception-v3 model in the dynasty-based classification of ancient murals. EURASIP Journal on Advances in Signal Processing 2021:49.
Ferlay J., Ervik M., Lam F., Colombet M., Mery L., Piñeros M., Znaor A., Soerjomataram I., Bray F. (2020): Global Cancer Observatory: Cancer Today. Lyon, France: International Agency for Research on Cancer. https://gco.iarc.fr/today/explore.
Godlewski A., Czajkowski M., Mojsak P., Pienkowski T., Gosk W., Lyson T., Mariak Z., Reszec J., Kondraciuk M., Kaminski K., Kretowski M., Moniuszko M., Kretowski A., Ciborowski M. (2023): A comparison of different machine-learning techniques for the selection of a panel of metabolites allowing early detection of brain tumors. Scientific Reports 13, 11044.
Harris C. R., Millman K. J., van der Walt S. J., Gommers R., Virtanen P., Cournapeau D., Wieser E., Taylor J., Berg S., Smith N. J., Kern R., Picus M., Hoyer S., van Kerkwijk M. H., Brett M., Haldane A., del Río J. F., Wiebe M., Peterson P., Gérard-Marchant P., Sheppard K., Reddy T., Weckesser W., Abbasi H., Gohlke C., Oliphant T. E. (2020): Array programming with NumPy. Nature 585(7825), 357–362.
Ji Q., Huang J., He W., Sun Y. (2019): Optimized deep convolutional neural networks for identification of macular diseases from optical coherence tomography images. Algorithms 12(3), 51.
Khanna C. (2020): Number of Parameters in a Feed-Forward Neural Network. https://towardsdatascience.com/number-of-parameters-in-a-feed-forward-neural-network-4e4e33a53655 [accessed: Oct 25, 2023].
Kubera E., Kubik-Komar A., Kurasiński P., Piotrowska-Weryszko K., Skrzypiec M. (2022): Detection and recognition of pollen grains in multilabel microscopic images. Sensors 22, 2690.
Kumar Y., Dubey A.K., Arora R.R., Rocha A. (2020): Multiclass classification of nutrients deficiency of apple using deep neural network. Neural Computing and Applications 34(11), 8411–8422.
Miller K.D., Ostrom Q.T., Kruchko C., Patil N., Tihan T., Cioffi G., Fuchs H.E., Waite K.A., Jemal A., Siegel R.L., Barnholtz-Sloan J.S. (2021): Brain and other central nervous system tumor statistics, 2021. CA: A Cancer Journal for Clinicians 71(5), 381-406.
Nguyen T.-H., Nguyen T.-N., Ngo B.-V. (2022): A VGG-19 model with transfer learning and image segmentation for classification of tomato leaf disease. AgriEngineering 4(4), 871–887.
Ostrom Q.T., Price M., Neff C., Cioffi G., Waite K.A., Kruchko C., Barnholtz-Sloan J.S. (2022): CBTRUS statistical report: Primary brain and other central nervous system tumors diagnosed in the United States in 2015–2019. Neuro-Oncology 24, v1-v95.
Reszke M. (2023): Machine learning methods in the detection of a brain tumor. Master’s thesis in Data Science. Adam Mickiewicz University, Poznań (in Polish).
Simonyan K., Zisserman A. (2014): Very deep convolutional networks for large-scale image recognition. https://arxiv.org/abs/1409.1556 [accessed: Oct 30, 2023].
Tan M., Le Q.V. (2019): EfficientNet: Rethinking model scaling for convolutional neural networks. https://arxiv.org/abs/1905.11946 [accessed: Oct 30, 2023].
Teng G., Wang Q., Yang H., Qi X., Zhang H., Cui X., Idrees B.S., Xiangli W., Wei K., Khan M.N. (2020): Pathological identification of brain tumors based on the characteristics of molecular fragments generated by laser ablation combined with a spiking neural network. Biomedical Optics Express 11(8), 4276-4289.
Vellaichamy A. S., Swaminathan A., Varun C., Kalaivani S. (2021): Multiple plant leaf disease classification using DenseNet-121 architecture. International Journal of Electrical Engineering and Technology 12(5), 38–57.
Ye Z., Srinivasa K., Meyer A., Sun P., Lin J., Viox J. D., Song C., Wu A. T., Song S.-K., Dahiya S., Rubin J. B. (2021): Diffusion histology imaging differentiates distinct pediatric brain tumor histology. Scientific Reports 11(1), 4749.