Cancer is the second leading cause of death globally, and breast cancer (BC) is the second most reported cancer. Although the incidence rate is reducing in developed countries, the reverse is the case in low- and middle-income countries. Early detection has been found to contain cancer growth, prevent metastasis, ease treatment, and reduce mortality by 25%. The digital mammogram is one of the most common, cheapest, and most effective BC screening techniques capable of early detection of up to 90% BC incidence. However, the mammogram is one of the most difficult medical images to analyze. In this paper, we present a method of training a deep learning model for BC diagnosis. We developed a discriminative fine-tuning method which dynamically assigns different learning rates to each layer of the deep CNN. In addition, the model was trained using mixed-precision training to ease the computational demand of training deep learning models. Lastly, we present data augmentation methods for mammograms. The discriminative fine-tuning algorithm enables rapid convergence of the model loss; hence, the models were trained to attain their best performance within 50 epochs. Comparing the results, DenseNet achieved the highest accuracy of 0.998, while AlexNet obtained 0.988.

Breast cancer (BC) is currently the most common form of cancer diagnosed worldwide with an incidence estimated at 2.26 million in 2020. Additionally, BC is the leading cause of cancer death. Many subtypes of breast cancer exist with distinct biological features and which respond differently to various treatment modalities and have different clinical outcomes. To ensure that sufferers receive lifesaving patients-tailored treatment early, it is crucial to accurately distinguish dangerous malignant (ductal carcinoma, lobular carcinoma, mucinous carcinoma, and papillary carcinoma) subtypes of tumors from adenosis, fibroadenoma, phyllodes tumor, and tubular adenoma benign harmless subtypes. An excellent automated method for detecting malignant subtypes of tumors is desirable since doctors do not identify 10% to 30% of breast cancers during regular examinations. While several computerized methods for breast cancer classification have been proposed, deep convolutional neural networks (DCNNs) have demonstrated superior performance. In this work, we proposed an ensemble of four variants of DCNNs combined with the support vector machines classifier to classify breast cancer histopathological images into eight subtypes classes: four benign and four malignant. The proposed method utilizes the power of DCNNs to extract highly predictive multi-scale pooled image feature representation (MPIFR) from four resolutions (40×, 100×, 200×, and 400×) of BC images that are then classified using SVM. Eight pre-trained DCNN architectures (Inceptionv3, InceptionResNetv2, ResNet18, ResNet50, DenseNet201, EfficientNetb0, shuffleNet, and SqueezeNet) were individually trained and an ensemble of the four best-performing models (ResNet50, ResNet18, DenseNet201, and EfficientNetb0) was utilized for feature extraction. One-versus-one SVM classification was then utilized to model an 8-class breast cancer image classifier. Our work is novel because while some prior work has utilized CNNs for 2- and 4-class breast cancer classification, only one other prior work proposed a solution for 8-class BC histopathological image classification. A 6B-Net deep CNN model was utilized, achieving an accuracy of 90% for 8-class BC classification. In rigorous evaluation, the proposed MPIFR method achieved an average accuracy of 97.77%, with 97.48% sensitivity, and 98.45% precision on the BreakHis histopathological BC image dataset, outperforming the prior state-of-the-art for histopathological breast cancer multi-class classification and a comprehensive set of DCNN baseline models.

The recent advancements in Information and Communication Technology (ICT) as well as increasing demand for vehicular safety has led to significant progressions in Autonomous Vehicle (AV) technology. Perception and Localisation are major operations that determine the success of AV development and usage. Therefore, significant research has been carried out to provide AVs with the capabilities to not only sense and understand their surroundings efficiently, but also provide detailed information of the environment in the form of 3D maps. Visual Simultaneous Localisation and Mapping (V-SLAM) has been utilised to enable a vehicle understand its surroundings, map the environment, and identify its position within the area. This paper presents a detailed review of V-SLAM techniques implemented for AV perception and localisation. An overview of SLAM techniques is presented. In addition, an in-depth review is conducted to highlight various V-SLAM schemes, their strengths, and limitations. Challenges associated with V-SLAM deployment and future research directions are also provided in this paper.

We address the problem of crowd estimation in situations such as indoor events using anonymous and non-participatory CSI of WiFi Signals. Observing the great resemblance of Channel State Information (CSI, a finegrained information captured from the received Wi-Fi signal) to texture, we propose a brand-new framework based on statistical mechanics, and relying only on sets of machine learning techniques.In this paper, a framework for crowd count estimation is presented which utilizes Chebyshev filter and SVD to remove background noise in the CSI data, PCA to reduce the dimensionality of the CSI data and spectral descriptors for feature extraction. From the extracted feature, a set of classiffying algorithms are then utilised for training and testing the accuracy of our crowd estimation framework The aim of this framework to effectively and efficiently extract the channel information in WiFi signals across OFDM carriers reflected by the presence of human bodies. From the experiments conducted, we demonstrate the feasibility and efficacy of the proposed framework. Our result depict that our estimation becomes more–rather than less–accurate when the crowd count increases.

Breast cancer accounts for 30% of all female cancers. Accurately distinguishing dangerous malignant tumors from benign harmless ones is key to ensuring patients receive lifesaving treatments on time. However, as doctors currently do not identify 10% to 30% of breast cancers during regular assessment, automated methods to detect malignant tumors are desirable. Although several computerized methods for breast cancer classification have been proposed, convolutional neural networks (CNNs) have demonstrably outperformed other approaches. In this paper, we propose an automated method for the binary classification of breast cancer tumors as either malignant or benign that utilizes a bag of deep multi-resolution convolutional features (BoDMCF) extracted from histopathological images at four resolutions (40×, 100×, 200× and 400×) by three pre-trained state-of-the-art deep CNN models: ResNet-50, EfficientNetb0, and Inception-v3. The BoDMCF extracted by the pre-trained CNNs were pooled using global average pooling and classified using the support vector machine (SVM) classifier. While some prior work has utilized CNNs for breast cancer classification, they did not explore using CNNs to extract and pool a bag of deep multi-resolution features. Other prior work utilized CNNs for deep multi-resolution feature extraction from chest X-ray radiographs to detect other conditions such as pneumoconiosis but not for breast cancer detection from histopathological images. In rigorous evaluation experiments, our deep BoDMCF feature approach with global pooling achieved an average accuracy of 99.92%, sensitivity of 0.9987, specificity (or recall) of 0.9797, positive prediction value (PPV) or precision of 0.99870, F1-Score of 0.9987, MCC of 0.9980, Kappa of 0.8368, and AUC of 0.9990 on the publicly available BreaKHis breast cancer image dataset. The proposed approach outperforms the prior state of the art for histopathological breast cancer classification as well as a comprehensive set of CNN baselines, including ResNet18, InceptionV3, DenseNet201, EfficientNetb0, SqueezeNet, and ShuffleNet, when classifying images at any individual resolutions (40×, 100×, 200× or 400×) or when SVM is used to classify a BoDMCF extracted using any single pre-trained CNN model. We also demonstrate through a carefully constructed set of experiments that each component of our approach contributes non-trivially to its superior performance including transfer learning (pre-training and fine-tuning), deep feature extraction at multiple resolutions, global pooling of deep multiresolution features into a powerful BoDMCF representation, and classification using SVM.

A comprehensive medical image-based diagnosis is usually performed across various image modalities before passing a final decision; hence, designing a deep learning model that can use any medical image modality to diagnose a particular disease is of great interest. The available methods are multi-staged, with many computational bottlenecks in between. This paper presents an improved end-to-end method of multimodal image classification using deep learning models. We present top research methods developed over the years to improve models trained from scratch and transfer learning approaches. We show that when fully trained, a model can first implicitly discriminate the imaging modality and then diagnose the relevant disease. Our developed models were applied to COVID-19 classification from chest X-ray, CT scan, and lung ultrasound image modalities. The model that achieved the highest accuracy correctly maps all input images to their respective modality, then classifies the disease achieving overall 91.07% accuracy.