A Comprehensive Review on Breast Cancer Detection, Classification and Segmentation Using Deep Learning

One of the most frequent cancers in women is breast cancer, and in the year 2022, approximately 287,850 new cases have been diagnosed. From them, 43,250 women died from this cancer. An early diagnosis of this cancer can help to overcome the mortality rate. However, the manual diagnosis of this cancer using mammogram images is not an easy process and always requires an expert person. Several AI-based techniques have been suggested in the literature. However, still, they are facing several challenges, such as similarities between cancer and non-cancer regions, irrelevant feature extraction, and weak training models. In this work, we proposed a new automated computerized framework for breast cancer classification. The proposed framework improves the contrast using a novel enhancement technique called haze-reduced local-global. The enhanced images are later employed for the dataset augmentation. This step aimed at increasing the diversity of the dataset and improving the training capability of the selected deep learning model. After that, a pre-trained model named EfficientNet-b0 was employed and fine-tuned to add a few new layers. The fine-tuned model was trained separately on original and enhanced images using deep transfer learning concepts with static hyperparameters’ initialization. Deep features were extracted from the average pooling layer in the next step and fused using a new serial-based approach. The fused features were later optimized using a feature selection algorithm known as Equilibrium-Jaya controlled Regula Falsi. The Regula Falsi was employed as a termination function in this algorithm. The selected features were finally classified using several machine learning classifiers. The experimental process was conducted on two publicly available datasets—CBIS-DDSM and INbreast. For these datasets, the achieved average accuracy is 95.4% and 99.7%. A comparison with state-of-the-art (SOTA) technology shows that the obtained proposed framework improved the accuracy. Moreover, the confidence interval-based analysis shows consistent results of the proposed framework.

https://www.mdpi.com/2075-4418/13/7/1238/pdf?version=1679967085

BC2NetRF: Breast Cancer Classification from Mammogram Images Using Enhanced Deep Learning Features and Equilibrium-Jaya Controlled Regula Falsi-Based Features Selection

The prevalence of security vulnerabilities in Internet of Things (IoT) applications poses a serious threat to enterprise systems, necessitating sophisticated and reliable defense solutions to counter emerging and evolving threats. For the Industrial Internet of Things (IIoT), stakeholders require trustworthy and sustainable systems that can prevent the loss of human life during critical operations. The impact of multi-variant persistent and sophisticated bot attacks on connected IIoTs is potentially catastrophic, and their detection presents a highly complex and critical challenge. Therefore, there is a pressing need for efficient and timely detection of IIoT botnet attacks. This research paper proposes a robust deep learning model named AttackNet for the detection and classification of different botnet attacks in IIoT based on adaptive based CNN-GRU model. The model is extensively evaluated using the latest dataset and standard performance evaluation metrics, demonstrating its capacity to protect IIoT networks against sophisticated cyber-attacks with a testing accuracy of 99.75%, a loss of 0.0063, precision and recall score of 99.75% and 99.74% respectively. Our proposed model demonstrates superior accuracy, particularly within the N_BaIoT dataset. It achieves an outstanding accuracy of 99.75% across ten classes, surpassing state-of-the-art techniques by a substantial margin ranging from 3.2% to 16.07%. Moreover, the proposed model outperforms state-of-the-art anomaly detection systems in IIoT based on a real-time IoT device dataset in terms of detecting and classifying botnet attacks accurately. 

Deep learning enabled intrusion detection system for Industrial IOT environment

This study aims to develop an efficient and accurate breast cancer classification model using meta-learning approaches and multiple convolutional neural networks. This Breast Ultrasound Images (BUSI) dataset contains various types of breast lesions. The goal is to classify these lesions as benign or malignant, which is crucial for the early detection and treatment of breast cancer. The problem is that traditional machine learning and deep learning approaches often fail to accurately classify these images due to their complex and diverse nature. In this research, to address this problem, the proposed model used several advanced techniques, including meta-learning ensemble technique, transfer learning, and data augmentation. Meta-learning will optimize the model’s learning process, allowing it to adapt to new and unseen datasets quickly. Transfer learning will leverage the pre-trained models such as Inception, ResNet50, and DenseNet121 to enhance the model’s feature extraction ability. Data augmentation techniques will be applied to artificially generate new training images, increasing the size and diversity of the dataset. Meta ensemble learning techniques will combine the outputs of multiple CNNs, improving the model’s classification accuracy. The proposed work will be investigated by pre-processing the BUSI dataset first, then training and evaluating multiple CNNs using different architectures and pre-trained models. Then, a meta-learning algorithm will be applied to optimize the learning process, and ensemble learning will be used to combine the outputs of multiple CNN. Additionally, the evaluation results indicate that the model is highly effective with high accuracy. Finally, the proposed model’s performance will be compared with state-of-the-art approaches in other existing systems’ accuracy, precision, recall, and F1 score.

https://www.mdpi.com/2075-4418/13/13/2242/pdf?version=1688152510

Breast Cancer Classification through Meta-Learning Ensemble Technique Using Convolution Neural Networks


In recent years, machine learning (ML) and deep learning (DL) have been the leading approaches to solving various challenges, such as disease predictions, drug discovery, medical image analysis, etc., in intelligent healthcare applications. Further, given the current progress in the fields of ML and DL, there exists the promising potential for both to provide support in the realm of healthcare. This study offered an exhaustive survey on ML and DL for the healthcare system, concentrating on vital state of the art features, integration benefits, applications, prospects and future guidelines. To conduct the research, we found the most prominent journal and conference databases using distinct keywords to discover scholarly consequences. First, we furnished the most current along with cutting-edge progress in ML-DL-based analysis in smart healthcare in a compendious manner. Next, we integrated the advancement of various services for ML and DL, including ML-healthcare, DL-healthcare, and ML-DL-healthcare. We then offered ML and DL-based applications in the healthcare industry. Eventually, we emphasized the research disputes and recommendations for further studies based on our observations.


Machine learning and deep learning-based approach in smart healthcare: Recent advances, applications, challenges and opportunities

Abstract Machine learning helps construct predictive models in clinical data analysis, predicting stock prices, picture recognition, financial modelling, disease prediction, and diagnostics. This paper proposes machine learning ensemble algorithms to forecast diabetes. The ensemble combines k-NN, Naive Bayes (Gaussian), Random Forest (RF), Adaboost, and a recently designed Light Gradient Boosting Machine. The proposed ensembles inherit detection ability of LightGBM to boost accuracy. Under fivefold cross-validation, the proposed ensemble models perform better than other recent models. The k -NN, Adaboost, and LightGBM jointly achieve 90.76% detection accuracy. The receiver operating curve analysis shows that $$k$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>k</mml:mi> </mml:math> -NN, RF, and LightGBM successfully solve class imbalance issue of the underlying dataset. 

/pdf/an-ensemble-of-light-gradient-boosting-machine-and-adaptive-2munxenj.pdf

An Ensemble of Light Gradient Boosting Machine and Adaptive Boosting for Prediction of Type-2 Diabetes

Abstract Intrusion detection ( ID ) methods are security frameworks designed to safeguard network information systems. The strength of an intrusion detection method is dependent on the robustness of the feature selection method. This study developed a multi-level random forest algorithm for intrusion detection using a fuzzy inference system. The strengths of the filter and wrapper approaches are combined in this work to create a more advanced multi-level feature selection technique, which strengthens network security. The first stage of the multi-level feature selection is the filter method using a correlation-based feature selection to select essential features based on the multi-collinearity in the data. The correlation-based feature selection used a genetic search method to choose the best features from the feature set. The genetic search algorithm assesses the merits of each attribute, which then delivers the characteristics with the highest fitness values for selection. A rule assessment has also been used to determine whether two feature subsets have the same fitness value, which ultimately returns the feature subset with the fewest features. The second stage is a wrapper method based on the sequential forward selection method to further select top features based on the accuracy of the baseline classifier. The selected top features serve as input into the random forest algorithm for detecting intrusions. Finally, fuzzy logic was used to classify intrusions as either normal, low, medium, or high to reduce misclassification. When the developed intrusion method was compared to other existing models using the same dataset, the results revealed a higher accuracy, precision, sensitivity, specificity, and F1-score of 99.46%, 99.46%, 99.46%, 93.86%, and 99.46%, respectively. The classification of attacks using the fuzzy inference system also indicates that the developed method can correctly classify attacks with reduced misclassification. The use of a multi-level feature selection method to leverage the advantages of filter and wrapper feature selection methods and fuzzy logic for intrusion classification makes this study unique. 

https://link.springer.com/content/pdf/10.1007/s44196-023-00205-w.pdf

A Multi-level Random Forest Model-Based Intrusion Detection Using Fuzzy Inference System for Internet of Things Networks

Optimized DPMZM Based RoF Link Against Fiber Impairments

Multi-Label Learning Model for Diabetes Disease Comorbidity

In this paper, a modified pulse shaping methodology has been analyzed for the improvement in the performance ultra-wideband (UWB) communication systems with standard IEEE 802.15.4a open outdoor channel conditions with multiuser interference. The analysis of the pulse shaping methodology is useful for the improvement of an outdoor location system with a reduced interference for coexisting communications networks. 

Uwb system performance improvement for ieee 802.15.4a open outdoor channel environment

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