The studies and applications of nanofibers have grown over the years. It was observed that the properties of the nanometer-scale yarns present advantages in applications in several areas, such as biomedical, energy storage and production, and applications involving water filtration. These materials are synthesized through a technical process and, for that reason, they are subject to the presentation of failures. The most common flaws are the formation of granules and pores. With the evolution of computing, applications that use machine learning resources can assist in detecting these failures. This work aims to evaluate and compare two different approaches to morphological analysis to see losses in nanofibers. Firstly, a data set was created using a Scanning Electron Microscope. After that, each image was analyzed by ImageJ software and by RNA solution. As a hypothesis, the article will assess whether the beads identification and the number of beads by the analog method are statistically similar (H0) or statistically different (H1) from the machine learning method. The preliminary results indicate that for the group that used 100 images and computer visualization, the analog method's accuracy was 7.23%. In order to accuracy increase, another test with 150 more distinct images was done, bringing a new result of 55.09%. The analysis time was considerably less when performed by the computational method. It was possible to conclude that the computational approach does not have the beads identification statistically similar to the analog way concerning the methodology used. Therefore, rejected H0. However, the directly proportional relationship of accuracy with the number of samples suggests that training with more various images can calibrate the algorithm.