## 6 Discussion  
The main aim of this study was to develop LWCNN for benign and malignant tissue image classification based on multilevel feature map analysis and show the effectiveness of the model. Moreover, we developed an EML voting method for the classification of non-handcrafted (extracted from the GAP layer of model 2) and handcrafted (extracted using OCLBP and IOCLBP). Generally, in DL, the features are extracted automatically from raw data and further processed for classification using a neural network approach. However, for ML algorithms, features are extracted manually using different mathematical formulae; these are also regarded as handcrafted features. A CNN is suitable for complex detection tasks, such as analyses of scattered and finely drawn patterns in data. Of particular interest, in the malignant and benign classification task, model 2 was more effective than model 1.Indeed, model 1 performed below expectation, such that we modified it to improve performance, resulting in model 2. The modification comprised removal of the fourth convolutional block, flattening layer, and sigmoid activation function, as well as alterations of filter number and kernel size. Moreover, GAP replaced flattening after the third convolutional block, minimizing overfitting by reducing the total number of parameters in the model. The softmax activation function replaced the sigmoid  
Figure 12: Cancer prediction using a binary labeled test dataset. Examples of images that were (**a**) correctly and (**b**) incorrectly classified, showing their actual and predicted labels. activation function in the third dense layer. These modifications, based on the multilevel feature map analysis, improved the overall accuracy and localization ability of tissue image classification. Furthermore, in this study, we have also compared our proposed CNN model with the well-known pre-trained models such as VGG-16, ResNet-50, Inception-V3, and DenseNet-121. Among these, DenseNet proved to give the highest accuracy of 95% followed by the Inception V3 with 94.6%. The pre-trained VGG-16 and ResNet-50 achieved 92% and 93%, respectively. Although DenseNet gained the highest accuracy among all the pre-trained models as well as our proposed model 2, it is not quite comparable with the motto of this paper. The ultimate goal of this paper was to develop a light-weighted CNN without a much-complicated structure with minimum possible convolutional layers and achieve better classification performance. Model 2 proved this hypothesis by achieving an overall accuracy of 94%. On the other hand, all the pre-trained models are well trained on a huge dataset (ImageNet) which includes 1000 classes. Therefore, it is evident that the classification of such models will be done accurately without much hassle. Nevertheless, the comparison of computational cost between the proposed LWCNN and other pre-trained models was performed to analyze the memory usage, trainable parameters, and learning (training and testing) time, shown in Table 7. First, according to the comparison Table 7, the number of trainable parameters used in the LWCNN model was reduced by more than 75% as compared to VGG-16, ResNet-50, and Inception-V3, and 2% as compared to DenseNet-121. Second, the memory usage of the proposed model was significantly less when compared to other models. Third, the time taken to train the proposed model was also drastically less. Among the pre-trained models, VGG-16 and ResNet-50 agree with the objective of this work. From Tables 5 and 7, it is evident that our LWCNN (model 2) is competitive and inexpensive, whereas, the state-of-art models were computationally expensive and achieved comparable results. Therefore, from this perspective, model 2 of our proposed work performed better than VGG-16 and ResNet-50 in terms of accuracy, besides employing a simple architecture. Through fine-tuning of the hyperparameters, the CNN layers were determined to be optimal using the validation and test datasets.