Collaborating Authors

Recognizing Malaria Cells Using Keras Convolutional Neural Network(CNN) MarkTechPost


Artificial Intelligence has vast-ranging attention and its utilization in the healthcare business or industry. As an intense learner and a Kaggle beginner, I chose to work on the Malaria Cells dataset to get a little hands-on experience and discover how to work with CNN, Keras, and pictures on the Kaggle platform. In many points I love about Kaggle is the extensive knowledge it exists in the form of Kernels and Discussions. Taking ideas and references from different kernels and specialists really assisted me in getting more skilled at creating highly accurate results. Take a look at other kernels and see their strategy to gain more insights for your own improvement and knowledge building.

Malaria Parasite Detection using a Convolutional Neural Network on the Cainvas Platform


Malaria is a life-threatening disease caused by parasites that are transmitted to people through the bites of infected female Anopheles mosquitoes. The World Health Organization states the following…

Building a Convolutional Neural Network


This article aims to explain Convolutional Neural Network and how to Build CNN using the TensorFlow Keras library. This article will discuss the following topics. Let's first discuss Convolutional Neural Network. Deep learning is a very significant subset of machine learning because of its high performance across various domains. Convolutional Neural Network (CNN), is a powerful image processing deep learning type often using in computer vision that comprises an image and video recognition along with a recommender system and natural language processing ( NLP).

Understanding Deep Neural Network Predictions for Medical Imaging Applications Artificial Intelligence

Computer-aided detection has been a research area attracting great interest in the past decade. Machine learning algorithms have been utilized extensively for this application as they provide a valuable second opinion to the doctors. Despite several machine learning models being available for medical imaging applications, not many have been implemented in the real-world due to the uninterpretable nature of the decisions made by the network. In this paper, we investigate the results provided by deep neural networks for the detection of malaria, diabetic retinopathy, brain tumor, and tuberculosis in different imaging modalities. We visualize the class activation mappings for all the applications in order to enhance the understanding of these networks. This type of visualization, along with the corresponding network performance metrics, would aid the data science experts in better understanding of their models as well as assisting doctors in their decision-making process.

Spatially Correlated Patterns in Adversarial Images Artificial Intelligence

Adversarial attacks have proved to be the major impediment in the progress on research towards reliable machine learning solutions. Carefully crafted perturbations, imperceptible to human vision, can be added to images to force misclassification by an otherwise high performing neural network. To have a better understanding of the key contributors of such structured attacks, we searched for and studied spatially co-located patterns in the distribution of pixels in the input space. In this paper, we propose a framework for segregating and isolating regions within an input image which are particularly critical towards either classification (during inference), or adversarial vulnerability or both. We assert that during inference, the trained model looks at a specific region in the image, which we call Region of Importance (RoI); and the attacker looks at a region to alter/modify, which we call Region of Attack (RoA). The success of this approach could also be used to design a post-hoc adversarial defence method, as illustrated by our observations. This uses the notion of blocking out (we call neutralizing) that region of the image which is highly vulnerable to adversarial attacks but is not important for the task of classification. We establish the theoretical setup for formalising the process of segregation, isolation and neutralization and substantiate it through empirical analysis on standard benchmarking datasets. The findings strongly indicate that mapping features into the input space preserves the significant patterns typically observed in the feature-space while adding major interpretability and therefore simplifies potential defensive mechanisms.