Malaria parasites pose a significant global health burden, causing widespread
suffering and mortality. Detecting malaria infection accurately is crucial for
effective treatment and control. However, existing automated detection
techniques have shown limitations in terms of accuracy and generalizability.
Many studies have focused on specific features without exploring more
comprehensive approaches. In our case, we formulate a deep learning technique
for malaria-infected cell classification using traditional CNNs and transfer
learning models notably VGG19, InceptionV3, and Xception. The models were
trained using NIH datasets and tested using different performance metrics such
as accuracy, precision, recall, and F1-score. The test results showed that deep
CNNs achieved the highest accuracy -- 97%, followed by Xception with an
accuracy of 95%. A machine learning model SVM achieved an accuracy of 83%,
while an Inception-V3 achieved an accuracy of 94%. Furthermore, the system can
be accessed through a web interface, where users can upload blood smear images
for malaria detection.

我们使用传统的 CNN 和迁移学习模型（VGG19、InceptionV3 和 Xception）来构建深度学习技术，用于疟疾感染细胞分类。测试结果显示，深度 CNN 具有最高的准确度（97%），其次是 Xception（95%）。用户可以通过网页界面上传血涂片图像进行疟疾检测。

基于机器学习和深度学习技术的自动化基于网络的疟疾检测系统

Automated Web-Based Malaria Detection System with Machine Learning and  Deep Learning Techniques

As the world continues to face the challenges of climate change, it is
crucial to consider the environmental impact of the technologies we use. In
this study, we investigate the performance and computational carbon emissions
of various transfer learning models for garbage classification. We examine the
MobileNet, ResNet50, ResNet101, and EfficientNetV2S and EfficientNetV2M models.
Our findings indicate that the EfficientNetV2 family achieves the highest
accuracy, recall, f1-score, and IoU values. However, the EfficientNetV2M model
requires more time and produces higher carbon emissions. ResNet50 outperforms
ResNet110 in terms of accuracy, recall, f1-score, and IoU, but it has a larger
carbon footprint. We conclude that EfficientNetV2S is the most sustainable and
accurate model with 96.41% accuracy. Our research highlights the significance
of considering the ecological impact of machine learning models in garbage
classification.

通过研究垃圾分类中各种迁移学习模型的性能和计算碳排放，我们发现 EfficientNetV2S 是最可持续和准确的模型，其准确率高达 96.41%。这项研究强调了考虑机器学习模型在垃圾分类中的生态影响的重要性。

通过迁移学习管理家庭垃圾

Managing Household Waste through Transfer Learning

The field of clinical image analysis has been applying transfer learning
models increasingly due to their less computational complexity, better accuracy
etc. These are pre-trained models that don't require to be trained from scratch
which eliminates the necessity of large datasets. Transfer learning models are
mostly used for the analysis of brain, breast, or lung images but other sectors
such as bone marrow cell detection or bone cancer detection can also benefit
from using transfer learning models, especially considering the lack of
available large datasets for these tasks. This paper studies the performance of
several transfer learning models for osteosarcoma tumour detection.
Osteosarcoma is a type of bone cancer mostly found in the cells of the long
bones of the body. The dataset consists of H&E stained images divided into 4
categories- Viable Tumor, Non-viable Tumor, Non-Tumor and Viable Non-viable.
Both datasets were randomly divided into train and test sets following an 80-20
ratio. 80% was used for training and 20\% for test. 4 models are considered for
comparison- EfficientNetB7, InceptionResNetV2, NasNetLarge and ResNet50. All
these models are pre-trained on ImageNet. According to the result,
InceptionResNetV2 achieved the highest accuracy (93.29%), followed by
NasNetLarge (90.91%), ResNet50 (89.83%) and EfficientNetB7 (62.77%). It also
had the highest precision (0.8658) and recall (0.8658) values among the 4
models.

本文探讨了使用多种迁移学习模型进行骨肉瘤检测的性能，结果显示使用 InceptionResNetV2 模型获得了最佳的准确度 (93.29%) 和精度 (0.8658)。

利用迁移学习模型进行骨肉瘤肿瘤检测

Osteosarcoma Tumor Detection using Transfer Learning Models

Breast cancer is one of the most common and dangerous cancers in women, while
it can also afflict men. Breast cancer treatment and detection are greatly
aided by the use of histopathological images since they contain sufficient
phenotypic data. A Deep Neural Network (DNN) is commonly employed to improve
accuracy and breast cancer detection. In our research, we have analyzed
pre-trained deep transfer learning models such as ResNet50, ResNet101, VGG16,
and VGG19 for detecting breast cancer using the 2453 histopathology images
dataset. Images in the dataset were separated into two categories: those with
invasive ductal carcinoma (IDC) and those without IDC. After analyzing the
transfer learning model, we found that ResNet50 outperformed other models,
achieving accuracy rates of 90.2%, Area under Curve (AUC) rates of 90.0%,
recall rates of 94.7%, and a marginal loss of 3.5%.

本研究使用 2453 个组织病理学图像数据集，分离有侵袭性导管癌（IDC）和无 IDC 的数据，分析了预训练的深度迁移学习模型如 ResNet50、ResNet101、VGG16 和 VGG19 在检测乳腺癌方面的应用，研究发现 ResNet50 模型的准确率可达 90.2％，曲线下面积（AUC）率为 90.0％，召回率为 94.7％，不适当损失为 3.5％。