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

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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.


Deep learning to identify Malaria cells using CNN on Kaggle

#artificialintelligence

Deep learning has vast ranging applications and its application in the healthcare industry always fascinates me. As a keen learner and a Kaggle noob, I decided to work on the Malaria Cells dataset to get some hands-on experience and learn how to work with Convolutional Neural Networks, Keras and images on the Kaggle platform. One of the many things I like about Kaggle is the immense knowledge it holds in the form of Kernels and Discussions. Taking cues and references from various kernels and experts really helped me get better at producing highly accurate results. Do look at other kernels and understand their approach to gain more insights for your own development and knowledge building.


Food Mnist Classification

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This data set consists of 10 food categories, with 5,000 images. For each class, 125 manually reviewed test images are provided as well as 375 training images. On purpose, the training images were not cleaned, and thus still contain some amount of noise. This comes mostly in the form of intense colors and sometimes wrong labels. All images were rescaled to have a maximum side length of 512 pixels.


Indian Currency Notes Classifier -- on cAInvas

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Currency notes have identifiers that allow the visually impaired to identify them easily. This is a learned skill. On the other hand, classifying them using images is an easier solution to help the visually impaired identify the currency they are dealing with. Here, we use pictures of different versions of the currency notes taken from different angles, with different backgrounds and covering different proportions. The dataset contains 195 images of 7 categories of Indian Currency Notes -- Tennote, Fiftynote, Twentynote, 2Thousandnote, 2Hundrednote, Hundrednote, 1Hundrednote.


Heartbeat Anomaly Detection

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According to a report of WHO, around 17.9 million people die each year due to Cardiovascular Diseases.Over the years it has been found that these deaths can be prevented if the diseases are diagnosed at an early stage and even the disease can be cured. Artificial Intelligence has been applied in various fields and one of them is AI for healthcare.We have seen AI practitioners coming up with solution for various disease diagnosis such as Cancer Detection, Detection of Diabetic Retinopathy and much more.The techniques used in these detections mostly involve Deep Learning. So, by combining our knowledge of deep learning and with its integration Iot we can develop a smart digital-stethoscope which can help in diagnosing anomalies in heartbeat in real-time and can help in classifying Cardio-diseases. While working in cAInvas one of its key features is UseCases Gallary.When working on any of its UseCases you don't have to look for data manually.As they have the feature to import your dataset to your workspace when you work on them.To load the data we just have to enter the following commands: As with all unstructured data formats, audio data has a couple of preprocessing steps which have to be followed before it is presented for analysis. Another way of representing audio data is by converting it into a different domain of data representation, namely the frequency domain.