## 4 Discussion
Conversely, the Random Forest considers the estimated probability when making a classification decision. This likelihood information gives a reliable and accurate grading list of label predictions. Moreover, using these likelihood values can facilitate the design of an effective rejection mechanism. The Random Forest method is highly promising for classification, as it is not only non-parametric [66], but also offers the ability to estimate the significance of individual variables in the classification and to process high-dimensional data simultaneously with high computational efficiency. Even in cases of multiple noisy features, Random Forest performs perfectly, so there is no need for feature selection. Most interestingly, it is known to have high ROC and classification accuracy compared to the existing classification algorithms [67]. Therefore, it can enhance the classification performance of the hybrid architecture after changing the output units in the InceptionV3 model. As such, this idea of combining this novel hybrid InceptionV3 architecture with the RF classifier will constitute a new avenue to explore so as to benefit from both higher accuracy and recall with a much lower FN rate. Moreover, the tweaking of hyperparameters, as well as the choice of parameters and activation function, are tedious tasks that drastically impact the result obtained from the DL, its computational efficiency and the model's learning accuracy. The employment of non-linear FAs can aid the model in discovering complex data, processing them, and learning by producing correct predictions. This urged us to elaborate comparison between these different AFs on our dataset, and the latter ones yielded dissimilar results, we remarked that the combination of the InceptionV3 with the Swish function outperformed the other models, providing better accuracy of up to 90.10% and a recall up to 0.9188. Furthermore, it was observed that when the InceptionV3 architecture was trained using different deep learning optimizers, the Inception network trained by the SGD optimizer achieved the highest Recall of 0.9753, suggesting that this combination of CNN architecture and optimization algorithms. The details are displayed in Figures 18 and 19 and Tables 7 and 8. One of them is that the present datasets do not include images collected and annotated from real-life scenarios. Thus, training is performed by using images captured in a controlled environment. Another constraint is that the existing proposed techniques cannot recognize multiple diseases in an image or several occurrences of the same disease in an image. Additionally, we faced many problems while building the ML models. For example, during the training of the KNN classifier, the number of neighbors must be defined by the user, and the model is highly sensitive to noise. Moreover, the main problem with the SVM algorithm is the choice of the right kernel function. Because for each dataset, a different kernel function produces different results. Meanwhile, DL models demand more learning time and computational resources, but they may attain higher prediction accuracy and higher generalization ability, indicating that deep learning has superior learning ability. Compared with conventional machine learning, it may automatically and efficiently retrieve features from an image. DL models can clearly differentiate the image with similar features that are troublesome for the conventional machine learning algorithms to recognize. As the computing cost of deep learning models increases sharply with the dataset size, the trade-off between the accuracy of the results and the computing cost remains challenging. Furthermore, traditional machine learning algorithms require subjective feature extraction to transform binary vectors into one-dimensional vectors. In contrast, deep learning models can objectively extract features and directly process two-dimensional image data. In a nutshell, deep learning model is better suited for modeling image data.