该项目建立了一个用于在超声图像中对乳腺癌进行分类的CNN模型。实现97%的准确性，它详细介绍了模型的开发、测试和未来的改进
This project builds a CNN model for classifying breast cancer in ultrasound images. Achieving 97% accuracy, it details the model s development, testing, and future improvements (2024-03-05, Jupyter Notebook, 0KB, 下载0次)
分析Matrigel液滴中生长的结肠样体的brighfield图像，以提取形态学特征和物体计数。包括缝合功能。
Analysis of brighfield images of colonoids growing in Matrigel droplets to extract morphological features and object counts. Stitching funcitonality included. (2024-02-28, Cool, 0KB, 下载0次)
通过python工具GD.A1从培养皿中计算筛选绿色菌落。
Computational screening of green colour colonies from petri dish through python tool GD.A1. (2023-11-21, Python, 0KB, 下载0次)
从超声图像中提取胆囊，并转换为二进制掩模，
Extract gall bladder from an ultrasound image and convert to binary mask, (2022-07-04, Python, 0KB, 下载0次)
采用10个CNN块，50个周期，进行ECG分类，精度和准确度为0.97，，
ECG classification using 10 CNN blocks, with 50 epochs, got a precision and accuracy of 0.97,, (2023-08-28, Others, 0KB, 下载0次)
给定id,从[https: www.ncbi.nlm.nih.gov获得对应的host](https: www.ncbi.nlm.nih.gov%E8%8E%B7%E5%BE%97%E5%AF%B9%E5%BA%94%E7%9A%84host),
Given the ID, obtain the corresponding host from [https: www.ncbi. nlm. nih. gov] (https: www.ncbi. nlm. nih. gov% E8% 8E% B7% E5% BE% 97% E5% AF% B9% E5% BA% 94% E7% 9A% 84host), (2021-09-03, JavaScript, 0KB, 下载0次)
海滨SAR算法
SAR algorithm for Beachfront (2018-08-22, Python, 3745KB, 下载0次)
高效的基于基因表达（微阵列数据）和ML-DL算法的准确分类方法...
Here, we describe the comparison of the most used algorithms in classical ML and DL to classify carcinogenic tumors described on 11_tumor data base, obtaining accuracies between 76.97% and 100% for tumor identification. Our results bring up a more efficient an accurate classification method based on gene expression (microarray data) and ML/DL (2020-04-13, Jupyter Notebook, 4487KB, 下载0次)
COVID-19-CT-Diagnose，预训练（imagenet）ResNet50，3种分类。测试准确率达到97.2%。使用渐变CAM进行感染区域检测...
Pretrained (imagenet) ResNet50 with 3 classification. Achieves 97.2% test accuracy. Use Grad-CAM for infected area segmentation. (2023-05-14, Jupyter Notebook, 6619KB, 下载0次)
COVID19 X射线分类器，一种使用X射线或CT-SCAN对阳性或阴性COVID19进行分类的分类器，准确率为99%，召回率为97%（macr...
A classifier that takes X-RAY or CT-SCAN to classify positive or negative COVID19 with 99% accuracy, 97% recall (macro avg) and 91% precision (macro avg). (2023-05-14, Jupyter Notebook, 2264KB, 下载0次)