Optimizing Deep Learning Models with Custom ReLU for Breast Cancer Histopathology Image Classification

Activation functions facilitate deep neural networks by introducing non-linearity to the learning process. The non-linearity feature gives the neural network the ability to learn complex patterns. Recently, the most widely used activation function is the Rectified Linear Unit (ReLU). Though, other various existing activation including hand-designed alternatives to ReLU have been proposed. However, none has succeeded in replacing ReLU due to their existing inconsistencies. In this work, activation function called ReLUMemristor-like Activation Function (RMAF) is proposed to leverage benefits of negative values in neural networks. RMAF introduces a constant parameter (α) and a threshold parameter (p) making the function smooth, non-monotonous, and introduces non-linearity in the network. Our experiments show that, the RMAF works better than ReLU and other activation functions on deeper models and across number of challenging datasets. Firstly, experiments are performed by training and classifying on multi-layer perceptron (MLP) over benchmark data such as the Wisconsin breast cancer, MNIST, Iris and Car evaluation. RMAF achieves high performance of 98.74%, 99.67%, 98.81% and 99.42% respectively, compared to Sigmoid, Tanh and ReLU. Secondly, experiments were performed on convolution neural network (ResNet) over MNIST, CIFAR-10 and CIFAR-100 data and observed the proposed activation function achieves higher performance accuracy of 99.73%, 98.77% and 79.82% respectively than Tanh, ReLU and Swish. Additionally, we experimented our work on deep networks i.e. squeeze network (SqueezeNet), Dense connected neural network (DenseNet121) and ImageNet dataset, which RMAF produced the best performance. We note that, the RMAF converges faster than the other functions and can replace ReLU in any neural network due to the efficiency, scalability and its similarity to both ReLU and Swish.

In deep learning, the choice of activation function plays a vital role in enhancing model performance. We propose AHerfReLU, a novel activation function that combines the rectified linear unit (ReLU) function with the error function (erf), complemented by a regularization term 1/(1 + x 2 ), ensuring smooth gradients even for negative inputs. The function is zero centered, bounded below, and nonmonotonic, offering significant advantages over traditional activation functions like ReLU. We compare AHerfReLU with 10 adaptive activation functions and state‐of‐the‐art activation functions, including ReLU, Swish, and Mish. Experimental results show that replacing ReLU with AHerfReLU leads to 3.18% improvement in Top‐1 accuracy on the LeNet network for the CIFAR100 dataset, 0.63% improvement on CIFAR10%, and 1.3% improvement in mean average precision (mAP) on the SSD300 model in the Pascal VOC dataset. Our results demonstrate that AHerfReLU enhances model performance, offering improved accuracy, loss reduction, and convergence stability. The function outperforms existing activation functions, providing a promising alternative for deep learning tasks.

The convolutional neural networks (CNN), including AlexNet, GoogleNet, VGGNet, etc. extract features for many computer vision problems which are very discriminative. The trained CNN model over one dataset performs reasonably well whereas on another dataset of similar type the hand-designed feature descriptor outperforms the same trained CNN model. The Rectified Linear Unit (ReLU) layer discards some values in order to introduce the non-linearity. In this paper, it is proposed that the discriminative ability of deep image representation using trained model can be improved by Average Biased ReLU (AB-ReLU) at the last few layers. Basically, AB-ReLU improves the discriminative ability in two ways: 1) it exploits some of the discriminative and discarded negative information of ReLU and 2) it also neglects the irrelevant and positive information used in ReLU. The VGGFace model trained in MatConvNet over the VGG-Face dataset is used as the feature descriptor for face retrieval over other face datasets. The proposed approach is tested over six challenging, unconstrained and robust face datasets (PubFig, LFW, PaSC, AR, FERET and ExtYale) and also on a large scale face dataset (PolyUNIR) in retrieval framework. It is observed that the AB-ReLU outperforms the ReLU when used with a pre-trained VGGFace model over the face datasets. The validation error by training the network after replacing all ReLUs with AB-ReLUs is also observed to be favorable over each dataset. The AB-ReLU even outperforms the state-of-the-art activation functions, such as Sigmoid, ReLU, Leaky ReLU and Flexible ReLU over all seven face datasets.

Neural networks with ReLU powers need less depth

Breast cancer is a common and fatal disease among women worldwide. Therefore, the early and precise diagnosis of breast cancer plays a pivotal role to improve the prognosis of patients with this disease. Several studies have developed automated techniques using different medical imaging modalities to predict breast cancer development. However, few review studies are available to recapitulate the existing literature on breast cancer classification. These studies provide an overview of the classification, segmentation, or grading of many cancer types, including breast cancer, by using traditional machine learning approaches through hand-engineered features. This review focuses on breast cancer classification by using medical imaging multimodalities through state-of-the-art artificial deep neural network approaches. It is anticipated to maximize the procedural decision analysis in five aspects, such as types of imaging modalities, datasets and their categories, pre-processing techniques, types of deep neural network, and performance metrics used for breast cancer classification. Forty-nine journal and conference publications from eight academic repositories were methodically selected and carefully reviewed from the perspective of the five aforementioned aspects. In addition, this study provided quantitative, qualitative, and critical analyses of the five aspects. This review showed that mammograms and histopathologic images were mostly used to classify breast cancer. Moreover, about 55% of the selected studies used public datasets, and the remaining used exclusive datasets. Several studies employed augmentation, scaling, and image normalization pre-processing techniques to minimize inconsistencies in breast cancer images. Several types of shallow and deep neural network architecture were employed to classify breast cancer using images. The convolutional neural network was utilized frequently to construct an effective breast cancer classification model. Some of the selected studies employed a pre-trained network or developed new deep neural networks to classify breast cancer. Most of the selected studies used accuracy and area-under-the-curve metrics followed by sensitivity, precision, and F-measure metrics to evaluate the performance of the developed breast cancer classification models. Finally, this review presented 10 open research challenges for future scholars who are interested to develop breast cancer classification models through various imaging modalities. This review could serve as a valuable resource for beginners on medical image classification and for advanced scientists focusing on deep learning-based breast cancer classification through different medical imaging modalities.

Recently, Deep learning has made a great deal of success in processing images, audios, and natural languages and so on. The activation function is one of the key factors in Deep learning. In this paper, according to characteristics of biological neurons, an improved Leaky Single-Peaked Triangle Linear Unit (LSPTLU) activation function is presented for the right-hand response unbounded of Rectified Linear Unit (ReLU) and Leaky ReLU (LReLU). LSPTLU is more in line with the biological neuron essence and achieves the excellent performance of equivalent or beyond ReLU and LReLU on different datsets, e.g., MNIST, Fashion-MNIST, SVHN, IMAGENET, CALTECH101 and CIFAR10 datasets.

Plant disease identification plays a crucial role in agricultural management, but the traditional methods are time-consuming and imprecise. This work contributes to the ongoing efforts to develop reliable and efficient solutions for automated plant disease diagnosis, ultimately aiding in the timely management and mitigation of agricultural challenges. This study investigated the effectiveness of various deep convolutional neural networks (CNNs) for automated plant disease identification from leaf images. By exploring deep and transfer learning techniques such as CNNs, InceptionNet, DenseNet 121, and ResNet-50, a Deep Convolutional Neural Network (DCNN) is proposed to categorize the leaf disease. Different activation functions, specifically Rectified Linear Unit (ReLU) and Swish, are employed to investigate their impact on model performance. The experiments revealed that the DCNN architecture, when paired with the Swish activation function, demonstrated 96% accuracy in plant disease identification.

Breast cancer is the most prevalent cancer globally and presents a significant threat to human health. The low survival rate for advanced stages highlights the importance of early and accurate diagnosis. Pathology images, providing insights into cancer's cellular aggressiveness, are crucial for breast cancer diagnosis. Advances in deep learning have transformed automated medical image diagnosis. However, traditional deep learning methods, reliant on large labeled datasets, face challenges with the typically small size of medical image datasets. To overcome this, our study combines deep transfer learning (DTL) with convolutional neural networks(CNN). We utilize and enhance a ResNet50 model pretrained on a large dataset. Additionally, we apply four different deep transfer learning methods to feature extraction and classification of the BreakHis dataset, aiming to determine if breast cancer is benign or malignant. Ultimately, our system achieved the best average accuracy rates under four different magnifications: 98.57%, 96.83%, 97.76%, and 97.16%. These results demonstrate the accuracy and potential of deep transfer learning in classifying breast cancer histopathological images and its promising application in tumor prediction.

The choice of activation function can have a large effect on the performance of a neural network. While there have been some attempts to hand-engineer novel activation functions, the Rectified Linear Unit (ReLU) remains the most commonly-used in practice. This paper shows that evolutionary algorithms can discover novel activation functions that outperform ReLU. A tree-based search space of candidate activation functions is defined and explored with mutation, crossover, and exhaustive search. Experiments on training wide residual networks on the CIFAR-10 and CIFAR-100 image datasets show that this approach is effective. Replacing ReLU with evolved activation functions results in statistically significant increases in network accuracy. Optimal performance is achieved when evolution is allowed to customize activation functions to a particular task; however, these novel activation functions are shown to generalize, achieving high performance across tasks. Evolutionary optimization of activation functions is therefore a promising new dimension of metalearning in neural networks.

MF-OMKT: Model fusion based on online mutual knowledge transfer for breast cancer histopathological image classification

This study aims to contribute to the improvement of the network's performance through developing a novel activation function. Over time, many activation functions have been proposed in order to solve the issues of the previous functions. We note here more than 50 activation functions that have been proposed, some of them being very popular such as sigmoid, Rectified Linear Unit (ReLU), Swish, Mish but not only. The main idea of this study that stays behind our proposal is a simple one, based on a very popular function called Swish, which is a composition function, having in its componence sigmoid function and ReLU function. Starting from this activation function we decided to ignore the negative region in the way the Rectified Linear Unit does but being different than that one mentioned through a nonlinear curve assured by the Swish positive region. The idea has been come up from a current function called Soft Clipping. We tested this proposal on more datasets in Computer Vision on classification tasks showing its high potential, here we mention MNIST, Fashion-MNIST, CIFAR-10, CIFAR-100 using two popular architectures: LeNet-5 and ResNet20 version 1.

Classical Convolutional Neural Networks, or ConvNets, have been the benchmarks for most object classification and face recognition tasks despite suffering from major limitations such as the inability to capture spatial co-locality between data points and favoring invariance over equivariance. Hence, a hierarchical routing layered architecture called Capsule Networks was proposed to overcome shortcomings of ConvNets. Capsules replace average or max pooling techniques of ConvNets with dynamic routing abilities between lower level and higher level neural units which better capture hierarchical relationships within the data and introduced reconstruction regularization mechanisms which deals with equivariance properties. By overcoming existing limitations, Capsules have proven themselves to be potential benchmarks in object segmentation, detection and reconstruction. Capsules have achieved state of the art results on the fundamental MNIST (Modified National Institute of Standards and Technology) handwritten digit dataset by reducing the ConvNets test error benchmark of 0.39% to 0.25%. In order to further augment this distinction, we experimented with five activation units such as sigmoid, e-Swish, Swish, variants of Rectified Linear Units (ReLU) like Parametric ReLU (PReLU), leaky ReLU (lReLU) and Scaled Exponential Linear Units (SELU), on two fundamental datasets - MNIST and cifar10. Based on these experimental results, we establish that e-Swish, and ReLU variants better optimize the Capsule architecture as compared to the currently used ReLU activation function.

Analysis of histopathological images (HIs) is crucial for detecting breast cancer (BR). However, because they vary, it is still very difficult to extract well-designed elements. Deep learning (DL) is a recent development that is used to extract high-level features. However, DL techniques continue to confront several difficult problems, such as the need for sufficient training data for DL models, which reduces the classification findings. In this study, an ensemble deep transfer convolutional neural network is presented to address this problem. The pre-trained models (ResNet50 and MobileNet) are employed to extract high-level features by freezing the front layer parameters while fine-tuning the last layers. In the proposed ensemble framework, KNN, SVM, logistic regression and neural networks are used as base classifiers. The majority vote and product approaches are used to integrate the predictions of each separate classifier. In the benchmark BreaKHis dataset, the suggested ensemble model is compared to some current approaches. It demonstrates that while the ensemble model obtains a considerable accuracy of 97.72% for the multiclass classification test, it achieves an accuracy of 99.2% for the binary task. The suggested ensemble model’s effectiveness in extracting useful features for BR images is demonstrated by comparison with existing cutting-edge models.

Different parameters of a fully convolutional network (FCN) are experimented to evaluate which combination predicts sound velocity models from a single configuration of seismic modeling. The evaluation is made considering some fixed parameters of the deep learning model, such as number of epochs, batch size and loss function, but with variations of the optimizer and activation function. The considered optimizers were RMSprop, Adam and Adamax, whilst the activations functions were the Rectified Linear Unit (ReLU), Leaky ReLU, Exponential Linear Unit (ELU) and Parametric ReLU (PReLU). Five metrics were used to evaluate the model during the testing stage: R2, Pearson's r, factor of two, mean absolute error and mean squared error. To the extent of these experiments, it was found that the optimizers have much more influence than the activation functions when determining the resolution of the output model. The best combination was the one using the PReLU activation function with the Adamax optimizer.

A convolutional neural network (CNN) is a subset of machine learning as well as one of the different types of artificial neural networks that are used for different applications and data types. Activation functions (AFs) are used in this type of network to determine whether or not its neurons are activated. One non-linear AF named as Rectified Linear Units (ReLU) which involves a simple mathematical operations and it gives better performance. It avoids rectifying vanishing gradient problem that inherents older AFs like tanh and sigmoid. Additionally, it has less computational cost. Despite these advantages, it suffers from a problem called Dying problem. Several modifications have been appeared to address this problem, for example; Leaky ReLU (LReLU). The main concept of our algorithm is to improve the current LReLU activation functions in mitigating the dying problem on deep learning by using the readjustment of values (changing and decreasing value) of the loss function or cost function while number of epochs are increased. The model was trained on the MNIST dataset with 20 epochs and achieved lowest misclassification rate by 1.2%. While optimizing our proposed methods, we received comparatively better results in terms of simplicity, low computational cost, and with no hyperparameters.