add the readme file for the Malaria_Detection

Author: dcslin

examples/healthcare/application/Malaria_Detection/readme.md

@@ -0,0 +1,44 @@
+<!--
+    Licensed to the Apache Software Foundation (ASF) under one
+    or more contributor license agreements.  See the NOTICE file
+    distributed with this work for additional information
+    regarding copyright ownership.  The ASF licenses this file
+    to you under the Apache License, Version 2.0 (the
+    "License"); you may not use this file except in compliance
+    with the License.  You may obtain a copy of the License at
+
+      http://www.apache.org/licenses/LICENSE-2.0
+
+    Unless required by applicable law or agreed to in writing,
+    software distributed under the License is distributed on an
+    "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+    KIND, either express or implied.  See the License for the
+    specific language governing permissions and limitations
+    under the License.
+-->
+
+# Singa for Malaria Detection Task
+
+## Malaria
+
+Malaria is caused by parasites and could be transmitted through infected mosquitoes. There are about 200 million cases worldwide, and about 400,000 deaths per year, therefore, malaria does lots of harm to global health.
+
+Although Malaria is a curable disease, inadequate diagnostics make it harder to reduce mortality, as a result, a fast and reliable diagnostic test is a promising and effective way to fight malaria.
+
+To mitigate the problem, we use Singa to implement a machine learning model to help with Malaria diagnosis. The dataset is from Kaggle https://www.kaggle.com/datasets/miracle9to9/files1?resource=download. Please download the dataset before running the scripts.
+
+## Structure
+
+* `malaria.py` in the `healthcare/data` directory is the scripts for preprocessing Malaria image datasets.
+
+* `mararia_net.py` in the `healthcare/models` directory includes the CNN model construction codes by creating
+  a subclass of `Module` to wrap the neural network operations 
+  of each model.
+
+* `train_cnn.py` is the training script, which controls the training flow by
+  doing BackPropagation and SGD update.
+
+## Command
+```bash
+python train_cnn.py cnn malaria -dir pathToDataset
+```