Deep Learning Transfer with AlexNet for chest X-ray COVID-19 recognition



alexnet, x-ray chest, convol, COVID-19, Deep Learning, recognition


The COVID-19 is a new disease from the virus SARS-CoV-2, the infection can cause respiratory illness with symptoms such as cough, fever, and, in severe cases, pneumonia. Early diagnosis is crucial for the correct treatment to reduce as much as possible the stress in the healthcare system. The need for auxiliary diagnostic tools has increased as there are no accurate automated toolkits available. Application of advanced artificial intelligence (AI) techniques coupled with radiological imaging can be helpful for the accurate detection of this disease. In this study, we have applied learning transfer to a convolutional neural network known as AlexNet for binary chest X-ray recognition (COVID-19 vs Healthy). We have fine-tunned AlexNet for our specific problem. The first layer, which works with RGB images, is replaced for images in a single intensity (grayscale). 11,312 chest X-ray images from six public databases were used to train the network. Among them are samples of healthy people and samples that present the effect of pneumonia and COVID-19 diseases. The results prove that deep learning with chest X-ray images can extract significant biomarkers related to COVID-19, since the obtained accuracy, sensitivity and specificity were 96.5%, 98.0%, and 91.7%, respectively. ROC analysis and confusion matrices are used to validate the results of the fine-tunned AlexNet network


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Author Biographies

Ernesto Cortés, Univesidad del Istmo

Ernesto Cortés Pérez recibió su grado de M. C. en Ciencias de la Computación por el Instituto Tecnológico de México, en el Laboratorio de Investigación en Tecnologías Inteligentes (LITI), Ha sido profesor en la Universidad del Istmo – Campus Tehuantepec en Oaxaca, México, en la carrera Ingeniería en Computación desde 2007, donde imparte tópicos relacionados al área de Inteligencia Artificial, sus actuales intereses incluyen Sistemas Inteligentes, Lógica Difusa, Redes Neuronales, Algoritmos Bio-inspirados, Deep Learning y Visión Artificial

Sergio Sánchez, Univesidad del Istmo

Sergio Sánchez Sánchez recibió su grado de Doctor por el Instituto Nacional de Astrofísica, Óptica y Electrónica (INAOE) en 2011. Desde 2014 ha sido investigador y profesor en la Universidad del Istmo en Oaxaca. Desde 2013 es parte del departamento de matemáticas aplicadas. Sus actuales intereses incluyen Óptica Cuántica, Probabilidad y Estadística Aplicada y Física Multidisciplinaria



How to Cite

Cortés, E., & Sánchez, S. (2021). Deep Learning Transfer with AlexNet for chest X-ray COVID-19 recognition. IEEE Latin America Transactions, 19(6), 944–951. Retrieved from



Special Issue on Fighting against COVID-19