Convolutional Neural Network-based image classification models are the current state-of-the-art for solving image classification problems. However, obtaining and using such a model to solve a specific image classification problem presents several challenges in practice. To train the model, we need to find good hyperparameter values for training, such as initial model weights or learning rate. However, finding these values is usually a non-trivial process. Another problem is that the training data used for model training is often class-imbalanced in practice. This usually has a negative impact on model training. However, not only is it challenging to obtain a Convolutional Neural Network-based model, but also to use the model after model training. After training, the model might be applied to images that were drawn from a data distribution that is different from the data distribution the training data was drawn from. These images are typically referred to as out-of-distribution samples. Unfortunately, Convolutional Neural Network-based image classification models typically fail to predict the correct class for out-of-distribution samples without warning, which is problematic when such a model is used for safety-critical applications. In my work, I examined whether information from the layers of a Convolutional Neural Network-based image classification model (pixels and activations) can be used to address all of these issues. As a result, I suggest a method for initializing the model weights based on image patches, a method for balancing a class-imbalanced dataset based on layer activations, and a method for detecting out-of-distribution samples, which is also based on layer activations. To test the proposed methods, I conducted extensive experiments using different datasets. My experiments showed that layer information (pixels and activations) can indeed be used to address all of the aforementioned challenges when training and using Convolutional Neural Network-based image classification models.