In the last few years, the deep learning (DL) computing paradigm has been deemed the Gold Standard in the machine learning (ML) community. Moreover, it has gradually become the most widely used computational approach in the field of ML, thus achieving outstanding results on several complex cognitive tasks, matching or even beating those provided by human performance. One of the benefits of DL is the ability to learn massive amounts of data. The DL field has grown fast in the last few years and it has been extensively used to successfully address a wide range of traditional applications. More importantly, DL has outperformed well-known ML techniques in many domains, e.g., cybersecurity, natural language processing, bioinformatics, robotics and control, and medical information processing, among many others. Despite it has been contributed several works reviewing the State-of-the-Art on DL, all of them only tackled one aspect of the DL, which leads to an overall lack of knowledge about it. Therefore, in this contribution, we propose using a more holistic approach in order to provide a more suitable starting point from which to develop a full understanding of DL. Specifically, this review attempts to provide a more comprehensive survey of the most important aspects of DL and including those enhancements recently added to the field. In particular, this paper outlines the importance of DL, presents the types of DL techniques and networks. It then presents convolutional neural networks (CNNs) which the most utilized DL network type and describes the development of CNNs architectures together with their main features, e.g., starting with the AlexNet network and closing with the High-Resolution network (HR.Net). Finally, we further present the challenges and suggested solutions to help researchers understand the existing research gaps. It is followed by a list of the major DL applications. Computational tools including FPGA, GPU, and CPU are summarized along with a description of their influence on DL. The paper ends with the evolution matrix, benchmark datasets, and summary and conclusion.

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Review of deep learning: concepts, CNN architectures, challenges, applications, future directions

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Deep Learning in Medical Image Analysis

Performance of deep learning vs machine learning in plant leaf disease detection

A deep learning approach to detect Covid-19 coronavirus with X-Ray images.

Hybrid deep learning for detecting lung diseases from X-ray images

Data analytics for novel coronavirus disease

Breast cancer is a common cancer for women. Early detection of breast cancer can considerably increase the survival rate of women. This paper mainly focuses on transfer learning process to detect breast cancer. Modified VGG (MVGG), residual network, mobile network is proposed and implemented in this paper. DDSM dataset is used in this paper. Experimental results show that our proposed hybrid transfers learning model (Fusion of MVGG16 and ImageNet) provides an accuracy of 88.3% where the number of epoch is 15. On the other hand, only modified VGG 16 architecture (MVGG 16) provides an accuracy 80.8% and MobileNet provides an accuracy of 77.2%. So, it is clearly stated that the proposed hybrid pre-trained network outperforms well compared to single architecture. This architecture can be considered as an effective tool for the radiologists in order to reduce the false negative and false positive rate. Therefore, the efficiency of mammography analysis will be improved.

/pdf/diagnosis-of-breast-cancer-based-on-modern-mammography-using-12j4vgo5ho.pdf

Diagnosis of Breast Cancer Based on Modern Mammography using Hybrid Transfer Learning

This paper focuses on the data-driven diagnosis of polycystic ovary syndrome (PCOS) in women. For this, machine learning algorithms are applied to a dataset freely available in Kaggle repository. This dataset has 43 attributes of 541 women, among which 177 are patients of PCOS disease. Firstly, univariate feature selection algorithm is applied to find the best features that can predict PCOS. The ranking of the attributes is computed and it is found that the most important attribute is the ratio of Follicle-stimulating hormone (FSH) and Luteinizing hormone (LH). Next, holdout and cross validation methods are applied to the dataset to separate the training and testing data. A number of classifiers such as gradient boosting, random forest, logistic regression, and hybrid random forest and logistic regression (RFLR) are applied to the dataset. Results show that the first 10 highest ranked attributed are good enough to predict the PCOS disease. Results also demonstrate that RFLR exhibits the best testing accuracy of 91.01% and recall value of 90% using 40-fold cross validation applied to the 10 most important features. Hence, RFLR is suitable for reliably classifying PCOS patients.

Diagnosis of Polycystic Ovary Syndrome Using Machine Learning Algorithms

At present world, Breast cancer is a second main cause of cancer death in women after lung cancer. Breast cancer occurs when some breast cells begin to raise abnormally. It can arise in any portion of the Breast and it can be prevented if the treatment is started at the early stage of the Breast cancer. Breast cancer is a malignant tumour i.e. a collection of cancer cells arising from the cells of the breast Treatment of breast cancer relies on the cancer type and its stage (zero to fourth) and may include surgery, radiation, or chemotherapy. Mainly this paper focused on diagnosing the Breast cancer disease using various classification algorithm with the help of data mining tools. Data mining of the intelligent accumulated from previously disease detected patients opened up a new aspect of medical progression. In this paper, the capability of the classification of Naive Bayes, Random Forest, Logistic Regression, Multilayer Perceptron, K-nearest neighbors in evaluating the Breast Cancer Disease dataset culled from UCI machine learning repository, was observed to predict the existence of Breast cancer. Data set has been explored in terms of Kappa Statistics, TP rate, FP Rate and precision.

Subrato Bharati

Papers

Hybrid deep learning for detecting lung diseases from X-ray images

Data analytics for novel coronavirus disease

Diagnosis of Breast Cancer Based on Modern Mammography using Hybrid Transfer Learning

Diagnosis of Polycystic Ovary Syndrome Using Machine Learning Algorithms

Breast Cancer Prediction Applying Different Classification Algorithm with Comparative Analysis using WEKA