MDFNet: application of multimodal fusion method based on skin image and clinical data to skin cancer classification

[1] Alizadeh SM(2021)Automatic skin cancer detection in dermoscopy images by combining convolutional neural networks and texture features Int J Imaging Syst Technol 31 695-707

[2] Mahloojifar A(2020)Multiple skin lesions diagnostics via integrated deep convolutional networks for segmentation and classification Comput Methods Programs Biomed 190 5162-5171

[3] Al-Masni MA(2017)A deep feature fusion methodology for breast cancer diagnosis demonstrated on three imaging modality datasets Med Phys 44 64270-64277

[4] Kim D-H(2018)Benchmark analysis of representative deep neural network architectures IEEE Access 6 474-478

[5] Kim T-S(2019)Dermoscopy image analysis: overview and future directions IEEE J Biomed Health Inform 23 1162-1168

[6] Antropova N(2022)A novel diagnostic method: FT-IR, Raman and derivative spectroscopy fusion technology for the rapid diagnosis of renal cell carcinoma serum Spectrochim Acta Part A Mol Biomol Spectrosc 269 778-789

[7] Huynh BQ(2001)Risk factors for basal cell carcinoma in a mediterranean population: role of recreational sun exposure early in life Arch Dermatol 137 155337-155351

[8] Giger ML(2020)Human action recognition using two-stream attention based LSTM networks Appl Soft Comput 86 4171-4418

[9] Bianco S(2010)Fusing visual and clinical information for lung tissue classification in HRCT data Artif Intell Med 149 67-74

[10] Cadene R(2021)Cancer statistics for the year 2020: an overview Int J Cancer 8 1735-1780

[1] Alizadeh SM(2021)Automatic skin cancer detection in dermoscopy images by combining convolutional neural networks and texture features Int J Imaging Syst Technol 31 695-707

[2] Mahloojifar A(2020)Multiple skin lesions diagnostics via integrated deep convolutional networks for segmentation and classification Comput Methods Programs Biomed 190 5162-5171

[3] Al-Masni MA(2017)A deep feature fusion methodology for breast cancer diagnosis demonstrated on three imaging modality datasets Med Phys 44 64270-64277

[4] Kim D-H(2018)Benchmark analysis of representative deep neural network architectures IEEE Access 6 474-478

[5] Kim T-S(2019)Dermoscopy image analysis: overview and future directions IEEE J Biomed Health Inform 23 1162-1168

[6] Antropova N(2022)A novel diagnostic method: FT-IR, Raman and derivative spectroscopy fusion technology for the rapid diagnosis of renal cell carcinoma serum Spectrochim Acta Part A Mol Biomol Spectrosc 269 778-789

[7] Huynh BQ(2001)Risk factors for basal cell carcinoma in a mediterranean population: role of recreational sun exposure early in life Arch Dermatol 137 155337-155351

[8] Giger ML(2020)Human action recognition using two-stream attention based LSTM networks Appl Soft Comput 86 4171-4418

[9] Bianco S(2010)Fusing visual and clinical information for lung tissue classification in HRCT data Artif Intell Med 149 67-74

[10] Cadene R(2021)Cancer statistics for the year 2020: an overview Int J Cancer 8 1735-1780