To help managing the large amount of biomedical images produced, image information retrieval tools have been developed to help accessing the right information at the right moment. To provide a test bed for image retrieval evaluation the ImageCLEFmed benchmark proposes a biomedical classification task that focuses on determining the image modality of figures from biomedical journal articles automatically. In the training data for this machine learning task some classes have many more images than others and thus a few classes are not well represented, which is a challenge for automatic image classification. To address this problem, an automatic training set expansion was first proposed. To improve the accuracy of the automatic training set expansion, a manual verification of the training set is done using the Crowdsourcing platform Crowdflower. This platform allows using external persons and pay for the crowdsourcing or use personal contacts free of charge. Crowdsourcing requires strict quality control or using trusted persons but it can quickly give access to a large number of judges and thus improve many machine learning tasks. Results show that the manual annotation of a large amount of biomedical images carried out in this work can help at image classification.