Timezone: »

 
Automatic Detection and Classification of Tick-borne Skin Lesions using Deep Learning
Matias Valdenegro-Toro

"Around the globe, ticks are the culprit of transmitting a variety of bacterial, viral and parasitic diseases. The incidence of tick-borne diseases has drastically increased within the last decade, with annual cases of Lyme disease soaring to an estimated 300,000 in the United States alone. As a result, more efforts in improving lesion identification approaches and diagnostics for tick-borne illnesses is critical. The objective for this study is to build upon the approach used by Burlina et al. by using a variety of convolutional neural network models to detect tick-borne skin lesions. We expanded the data inputs by acquiring images from Google in seven different languages to test if this would diversify training data and improve the accuracy of skin lesion detection. The final dataset included nearly 6,080 images and was trained on a combination of architectures. We obtained an accuracy of 80.72% with our model trained on the DenseNet 121 architecture."

Author Information

Matias Valdenegro-Toro (German Research Center for Artificial Intelligence)

More from the Same Authors

  • 2021 : Exploring the Limits of Epistemic Uncertainty Quantification in Low-Shot Settings »
    Matias Valdenegro-Toro
  • 2021 : Benchmark for Out-of-Distribution Detection in Deep Reinforcement Learning »
    Aaqib Parvez Mohammed · Matias Valdenegro-Toro
  • 2021 : Benchmark for Out-of-Distribution Detection in Deep Reinforcement Learning »
    Aaqib Parvez Mohammed · Matias Valdenegro-Toro
  • 2021 : Exploring the Limits of Epistemic Uncertainty Quantification in Low-Shot Settings »
    Matias Valdenegro-Toro
  • 2021 : Q&A Oral presentations »
    Matias Valdenegro-Toro · Andres Munoz · Johan Obando Ceron · Anil Batra
  • 2021 : Exploring the Limits of Epistemic Uncertainty Quantification in Low-Shot Settings »
    Matias Valdenegro-Toro
  • 2020 : QA Long Presentation II »
    Matias Valdenegro-Toro · Gefersom Lima · Nicolas Araque · Matías Molina
  • 2020 : Unsupervised Difficulty Estimation »
    Octavio Arriaga · Matias Valdenegro-Toro
  • 2019 : Poster session »
    Sebastian Farquhar · Erik Daxberger · Andreas Look · Matt Benatan · Ruiyi Zhang · Marton Havasi · Fredrik Gustafsson · James A Brofos · Nabeel Seedat · Micha Livne · Ivan Ustyuzhaninov · Adam Cobb · Felix D McGregor · Patrick McClure · Tim R. Davidson · Gaurush Hiranandani · Sanjeev Arora · Masha Itkina · Didrik Nielsen · William Harvey · Matias Valdenegro-Toro · Stefano Peluchetti · Riccardo Moriconi · Tianyu Cui · Vaclav Smidl · Taylan Cemgil · Jack Fitzsimons · He Zhao · · mariana vargas vieyra · Apratim Bhattacharyya · Rahul Sharma · Geoffroy Dubourg-Felonneau · Jonathan Warrell · Slava Voloshynovskiy · Mihaela Rosca · Jiaming Song · Andrew Ross · Homa Fashandi · Ruiqi Gao · Hooshmand Shokri Razaghi · Joshua Chang · Zhenzhong Xiao · Vanessa Boehm · Giorgio Giannone · Ranganath Krishnan · Joe Davison · Arsenii Ashukha · Jeremiah Liu · Sicong (Sheldon) Huang · Evgenii Nikishin · Sunho Park · Nilesh Ahuja · Mahesh Subedar · · Artyom Gadetsky · Jhosimar Arias Figueroa · Tim G. J. Rudner · Waseem Aslam · Adrián Csiszárik · John Moberg · Ali Hebbal · Kathrin Grosse · Pekka Marttinen · Bang An · Hlynur Jónsson · Samuel Kessler · Abhishek Kumar · Mikhail Figurnov · Omesh Tickoo · Steindor Saemundsson · Ari Heljakka · Dániel Varga · Niklas Heim · Simone Rossi · Max Laves · Waseem Gharbieh · Nicholas Roberts · Luis Armando Pérez Rey · Matthew Willetts · Prithvijit Chakrabarty · Sumedh Ghaisas · Carl Shneider · Wray Buntine · Kamil Adamczewski · Xavier Gitiaux · Suwen Lin · Hao Fu · Gunnar Rätsch · Aidan Gomez · Erik Bodin · Dinh Phung · Lennart Svensson · Juliano Tusi Amaral Laganá Pinto · Milad Alizadeh · Jianzhun Du · Kevin Murphy · Beatrix Benkő · Shashaank Vattikuti · Jonathan Gordon · Christopher Kanan · Sontje Ihler · Darin Graham · Michael Teng · Louis Kirsch · Tomas Pevny · Taras Holotyak