Breast Cancer Mammograms Classification Using Deep Neural Network and Entropy-Controlled Whale Optimization Algorithm

Abstract

Breast cancer has affected many women worldwide. To perform detection and classification of breast cancer many computer-aided diagnosis (CAD) systems have been established because the inspection of the mammogram images by the radiologist is a difficult and time taken task. To early diagnose the disease and provide better treatment lot of CAD systems were established. There is still a need to improve existing CAD systems by incorporating new methods and technologies in order to provide more precise results. This paper aims to investigate ways to prevent the disease as well as to provide new methods of classification in order to reduce the risk of breast cancer in women’s lives. The best feature optimization is performed to classify the results accurately. The CAD system’s accuracy improved by reducing the false-positive rates.The Modified Entropy Whale Optimization Algorithm (MEWOA) is proposed based on fusion for deep feature extraction and perform the classification. In the proposed method, the fine-tuned MobilenetV2 and Nasnet Mobile are applied for simulation. The features are extracted, and optimization is performed. The optimized features are fused and optimized by using MEWOA. Finally, by using the optimized deep features, the machine learning classifiers are applied to classify the breast cancer images. To extract the features and perform the classification, three publicly available datasets are used: INbreast, MIAS, and CBIS-DDSM. The maximum accuracy achieved in INbreast dataset is 99.7%, MIAS dataset has 99.8% and CBIS-DDSM has 93.8%. Finally, a comparison with other existing methods is performed, demonstrating that the proposed algorithm outperforms the other approaches.

Application of Artificial Intelligence to Ultrasonography

“…Di Segni et al investigated the diagnostic performance of S-Detect for breast lesions, demonstrating a sensitivity of 90% and a specificity of 70.8%, supporting its increased specificity (25). Using a deep learning-based transfer learning CNN model, Zahoor and coworkers identified benign and malignant breast lesions with an AUC of 93.6%, which could assist sonographers in classifying breast masses (26).…”

Diagnostic Strategies for Breast Cancer Detection: From Image Generation to Classification Strategies Using Artificial Intelligence Algorithms

“…Recently, DNN-based classification strategies have been proposed to maximize the accuracy that the classifiers achieve while reducing the computational resources required to perform its training and execution, being the physics-informed neural network or more recently, the Deep Kronecker neural network [ 190 ] are one of the most recent algorithms that have been proposed. In particular, these NNs are designed to take full advantage of the adaptive activation functions.…”

“…In particular, these NNs are designed to take full advantage of the adaptive activation functions. Traditional activation functions, such as the unipolar and bipolar sigmoid and the ReLU, might have problem when dealing with low-amplitude features as the training algorithm fails to achieve the lowest point in the error surface, thus generating classifiers prone to have generalization issues [ 190 ].…”

“…In this sense, by introducing a parameter into the activation function equations that can be modified during the training process, it can be avoided that the gradient function does not stall in a local minimum on the error surface [ 191 ]; thus, the highest accuracy can be obtained since the global minimum is reached [ 192 ]. The results presented [ 190 , 191 , 192 ] suggest that the utilization of this type of activation function might increase the classifier accuracy without increasing the computational burden required to train the network as the geometrical shape that the activation function defines can be adapted during the training time to the boundary decision zone where classification is required. It should be noted that the proposed Rowdy family of activation functions could be an interesting research topic for designing classification algorithms, as the presented results demonstrate that the lowest error is achieved in a prediction task.…”

An integrated framework for breast mass classification and diagnosis using stacked ensemble of residual neural networks

“…The CNN architecture model was initially suggested for image classification and has been the base of many popular state-of-the-art architectures such as ResNet, AlexNet, EfficientNet, VGG, etc. Consequently, many works have studied and applied the recent classification models for breast lesions classification, and have been employed in CAD systems in different methodologies such as using ensemble learning 23 , 24 , transfer learning 25 , 26 , and fusion modeling 27 – 29 .…”