## 2 Background  
Image recognition through convolutional neural networks has had a large number of applications, together with the previous image processing, in order to optimize pattern recognition [6]. Convolutional neural networks have had continuous improvements since their creation, through the innovation of new layers and making use of different computer vision techniques [7]. In the regenerative medicine field, a study carried out by [PERSON] [8] has developed automatic cell culture systems, where a CNN was implemented as a deep learning method to automate the recognition of cellular differences by means of the contrast in the different images. On the other hand, in China, a study was carried out where white blood cell segmentation was implemented, which proposed a method for segmentation based on color-space, making a color adjustment before segmentation, where an accuracy of 95.7% and an overall accuracy of 91.3% were achieved for the segmentation of the nucleus and segmentation of the cytoplasm [9]. On the other hand, based on studies carried out by Reference [10], randomized trials are more efficient for the optimization of multiple parameters than tests in a grid; for this reason, the Random Search method was selected for the optimization of CNN hyperparameters. With this method, four tasks are performed: (1) the use of the same model with different initial parameters; (2) the best models discovered through cross-validation are taken; (3) different control points for each of the models are identified, and finally, (4) execution of the average of the parameters in the training stage. For the implementation of Random Search in Python, the Talos library was used, which makes use of optimization algorithms such as Random Search, Grid Search, and correlation-based optimization. Talos has a POD strategy: Prepare, Optimize, and Deploy; this task is automated, and it produces results for prediction problems [5]. In addition, in a study carried out by [PERSON] [11], they proposed a convolutional neuronal network for the improvement of thermal images, incorporating the domain of brightness with a residual learning technique, increasing the performance and speed of convergence. The fast development of precision agriculture has generated the need for agriculture production management and estimation through the classification of crops through satellite images, but due to the complexity and fragmentation of the characteristics, traditional methods have not been able to fulfill the standards of agricultural problems. For this reason, in Reference [12], a classification method of agricultural remote sensing images was proposed based on convolutional neural networks, where the correct classification rate obtained was 99.55%. According to the above, neural networks, together with image processing, are a commonly used alternative for image classification [13]. Additionally, statistical methods have been identified to verify the efficiency of the segmentation method. For this case, the Jaccard coefficient and the Sorensen-Dice coefficient were used, which make comparisons of images (original and with image segmentation) through the bitmaps of each one:  
\[Jaccard(A,B)=\frac{|A\cap B|}{|A\cup B|}, \tag{1}\]  
with \(A\) and \(B\) being the bitmaps of the selected images. The coefficient takes the intersection between \(A\) and \(B\), which will be the points in common, and divides the union between \(A\) and \(B\), this being the totality of the two images without repetition of data. This throws a value between 0 and 1 that is known as the Jaccard coefficient [14], as shown in Equation (1). \[Dice(A,B)=\frac{2\mid A\cap B\mid}{|A|+|B|}, \tag{2}\]  
with \(A\) and \(B\) being the bitmaps of the selected images, we take the rule of the intersection between \(A\) and \(B\), which will be the points in common, multiplied by two; this is divided between the sum of the standard of \(A\) and the norm of \(B\), which yields a value between 0 and 1, which is known as the Sorensen-Dice coefficient [15], as shown in Equation (2).