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Recently, transforming windows files into images and its analysis using machine learning and deep learning have been considered as a state‐of‐the art works for malware detection and classification. This is mainly due to the fact that image‐based malware detection and classification is platform independent, and the recent surge of success of deep learning model performance in image classification. Literature survey shows that convolutional neural network (CNN) deep learning methods are successfully employed for image‐based windows malware classification. However, the malwares were embedded in a tiny portion in the overall image representation. Identifying and locating these affected tiny portions is important to achieve a good malware classification accuracy. In this work, a multi‐headed attention based approach is integrated to a CNN to locate and identify the tiny infected regions in the overall image. A detailed investigation and analysis of the proposed method was done on a malware image dataset. The performance of the proposed multi‐headed attention‐based CNN approach was compared with various non‐attention‐CNN‐based approaches on various data splits of training and testing malware image benchmark dataset. In all the data‐splits, the attention‐based CNN method outperformed non‐attention‐based CNN methods while ensuring computational efficiency. Most importantly, most of the methods show consistent performance on all the data splits of training and testing and that illuminates multi‐headed attention with CNN model's generalizability to perform on the diverse datasets. With less number of trainable parameters, the proposed method has achieved an accuracy of 99% to classify the 25 malware families and performed better than the existing non‐attention based methods. The proposed method can be applied on any operating system and it has the capability to detect packed malware, metamorphic malware, obfuscated malware, malware family variants, and polymorphic malware. In addition, the proposed method is malware file agnostic and avoids usual methods such as disassembly, de‐compiling, de‐obfuscation, or execution of the malware binary in a virtual environment in detecting malware and classifying malware into their malware family. [ABSTRACT FROM AUTHOR]

Simple Summary: According to skin disease reports by healthcare organizations, the number of cases of skin disease is growing gradually over the years globally. In skin disease diagnosis, dermatologists examine skin cells by using a dermatoscope. Due to the global shortage of expert dermatologists, mainly in developing countries, an accurate early skin disease diagnosis is not possible. To automate the examination of skin disease images, computer-aided diagnosis-based tools are used in healthcare and medical environments. Computer-aided diagnosis employs machine learning including deep learning models on skin disease images to detect and classify skin diseases. The present work proposes a deep learning-based model to accurately detect skin diseases and classify them into a family of skin diseases using skin disease images. The proposed system demonstrated a performance improvement of 4% accuracy for skin disease detection and 9% accuracy for skin disease classification compared to the existing deep learning-based models. The proposed computer-aided tool can be used as an early skin diagnosis tool to assist dermatologists in healthcare and medical environments. Deep learning-based models have been employed for the detection and classification of skin diseases through medical imaging. However, deep learning-based models are not effective for rare skin disease detection and classification. This is mainly due to the reason that rare skin disease has very a smaller number of data samples. Thus, the dataset will be highly imbalanced, and due to the bias in learning, most of the models give better performances. The deep learning models are not effective in detecting the affected tiny portions of skin disease in the overall regions of the image. This paper presents an attention-cost-sensitive deep learning-based feature fusion ensemble meta-classifier approach for skin cancer detection and classification. Cost weights are included in the deep learning models to handle the data imbalance during training. To effectively learn the optimal features from the affected tiny portions of skin image samples, attention is integrated into the deep learning models. The features from the finetuned models are extracted and the dimensionality of the features was further reduced by using a kernel-based principal component (KPCA) analysis. The reduced features of the deep learning-based finetuned models are fused and passed into ensemble meta-classifiers for skin disease detection and classification. The ensemble meta-classifier is a two-stage model. The first stage performs the prediction of skin disease and the second stage performs the classification by considering the prediction of the first stage as features. Detailed analysis of the proposed approach is demonstrated for both skin disease detection and skin disease classification. The proposed approach demonstrated an accuracy of 99% on skin disease detection and 99% on skin disease classification. In all the experimental settings, the proposed approach outperformed the existing methods and demonstrated a performance improvement of 4% accuracy for skin disease detection and 9% accuracy for skin disease classification. The proposed approach can be used as a computer-aided diagnosis (CAD) tool for the early diagnosis of skin cancer detection and classification in healthcare and medical environments. The tool can accurately detect skin diseases and classify the skin disease into their skin disease family. [ABSTRACT FROM AUTHOR]

Literature survey shows that convolutional neural network (CNN)‐based pretrained models have been successfully employed to diagnose and detect childhood pneumonia using chest X‐rays (CXR). However, most of the existing methods are prone to imbalance problems, which become even more significant in medical image classification for example most importantly childhood pneumonia classification using CXR. This is due to the fact that some classes in childhood pneumonia have a very little support in the training dataset. Additionally, though the existing methods have reported better performances for training and testing, in most of the test cases the existing models will not be effective on variants of the childhood pneumonia CXR images or CXR samples from a new pediatric patient. In addition, the models may be effective in detecting latent stage pediatric pneumonia but not show better performances for CXR samples from pediatric patients who are early stage, sick but not pneumonia, sick with other lung diseases, and so on. Generalization is an important term to be considered while designing a pneumonia classifier that can perform well on completely unseen pneumonia CXR datasets. This article presents a cost‐sensitive large‐scale learning with stacked ensemble meta‐classifier and transfer learning‐based deep feature fusion approach for pediatric pneumonia classification using CXR. With the aim to identify the importance among the classes of pneumonia, the larger cost items are introduced based on the class‐imbalance degree during the backpropogation learning methodology in transfer learning models such as Xception, InceptionResNetV2, DenseNet201, and NASNetMobile. Next, the features from the penultimate layer (global average pooling) of Xception, InceptionResNetV2, DenseNet201, and NASNetMobile were extracted and dimensionality of the extracted features were reduced using kernel principal component analysis (KPCA). The reduced features were fused together and passed into a stacked ensemble meta‐classifier for classifying the CXR into either pneumonia or normal. A stacked ensemble meta‐classifier is a two stage approach in which the first stage employs random forest and support vector machine (SVM) for prediction and followed by logistic regression for classification. Experiments of the proposed model were done on publicly available benchmark pediatric pneumonia classification CXR dataset. In addition, the experiments for existing methods as well as various cost‐insensitive models were conducted. In all the experiments, the proposed method has achieved better performances compared to the existing methods as well as various cost‐insensitive models. In particular, the proposed method showed 6% improvement in precision, 10% improvement in recall, 9% improvement in F1 score with less misclassification costs (0.0321) and accuracy (96.8%). Most importantly, the proposed method is insensitive to the imbalance data and more effective to handle variants of the childhood pneumonia CXR images. Thus, the proposed approach can be used as a tool for point‐of‐care diagnosis by healthcare professionals. [ABSTRACT FROM AUTHOR]

Cassava is a rich source of carbohydrates, and it is vulnerable to virus diseases. Literature survey shows that the image recognition and integrated deep learning approach is successfully employed for Cassava leaf disease classification. Mostly, transfer learning based on a convolutional neural network (CNN) models were successfully applied for Cassava leaf disease classification. However, existing approaches are not effective in identifying the tiny portion of the disease in the overall leaf area. Identifying and focussing on regions affected by the disease is vital to achieving a good classification accuracy. An attention‐based approach is integrated into pretrained CNN‐based EfficientNet models to locate and identify the tiny infected regions in Cassava leaf. Penultimate layer features of attention‐based EfficientNet models such as A_EfficientNetB4, A_EfficientNetB5, and A_EfficientNetB6 were extracted. Next, the dimensionality of the extracted features was reduced using kernel principal component analysis. The reduced features were fused and passed into a stacked ensemble meta‐classifier for Cassava leaf disease classification. A stacked ensemble meta‐classifier is a two‐stage approach in which the first stage employs random forest and support vector machine (SVM) for prediction followed by logistic regression for classification. Detailed investigation and analysis of the proposed method, attention, and non‐attention‐based approaches with CNN pretrained models were tested using a publicly available benchmark dataset of Cassava leaf disease images. The proposed method achieved better performances in all experiments than several existing methods as well as various attention and non‐attention‐based CNN pretrained models. The proposed approach can be used as a deployable tool for Cassava leaf disease classification in agricultural field. [ABSTRACT FROM AUTHOR]

The targeted malware attacks are usually created by few crime groups. They may essentially use their existing malware sample malicious code to rebuild the variants for sophistication and evade the malware detection. This trend emphasizes the importance of performing the malware family classification for applying the effective malware mitigation and prevention strategies. In this paper, we propose an efficient neural network model EfficientNetB1 to perform the malware family classification using the malware byte level image representation technique. To alleviate the computation resource consumption caused by deep learning (DL) models training and testing the various Convolutional Neural Network (CNN) based models, we have performed the performance and computational efficiency evaluation of the various CNN pretrained models to select the best CNN network architecture for malware classification. Additionally, the CNN pretrained models are evaluated against the different types of malware image representation methods, which are distinguished based on selection of the image width size. Our evaluation of the proposed model EfficientNetB1 shows that it has achieved an accuracy of 99% to classify the Microsoft Malware Classification Challenge (MMCC) malware classes using the malware image representation with fixed image width and also require fewer network parameters compared to other pretrained models to achieve the performance accuracy. Furthermore, various visualization techniques were used to compare the performances of the various CNN pretrained models. [ABSTRACT FROM AUTHOR]

Chest X-ray is the most commonly adopted non-invasive and painless diagnostic test for pediatric pneumonia. However, the low radiation levels for diagnosis make accurate detection challenging, and this initiates the need for an unerring computer-aided diagnosis model. Our work proposes stacking ensemble learning on features extracted from channel attention deep CNN architectures. The features extracted from the channel attention-based ResNet50V2, ResNet101V2, ResNet152V2, Xception, and DenseNet169 are individually passed through Kernel PCA for dimensionality reduction and concatenated. A stacking classifier with Support Vector Classifier, Logistic Regression, K-Nearest Neighbour, Nu-SVC, and XGBClassifier is employed for the final- Normal and Pneumonia classification. The stacking classifier achieves an accuracy of 96.15%, precision of 97.91%, recall of 95.90%, F1 score of 96.89%, and an AUC score of 96.24% on the publicly available pediatric pneumonia dataset. We expect this model to help the real-time diagnosis of pediatric pneumonia significantly. [ABSTRACT FROM AUTHOR]