Diabetic retinopathy is one of the most significant retinal diseases that can lead to blindness. As a result, it is critical to receive a prompt diagnosis of the disease. Manual screening can result in misdiagnosis due to human error and limited human capability. In such cases, using a deep learning-based automated diagnosis of the disease could aid in early detection and treatment. In deep learning-based analysis, the original and segmented blood vessels are typically used for diagnosis. However, it is still unclear which approach is superior. In this study, a comparison of two deep learning approaches (Inception v3 and DenseNet-121) was performed on two different datasets of colored images and segmented images. The study’s findings revealed that the accuracy for original images on both Inception v3 and DenseNet-121 equaled 0.8 or higher, whereas the segmented retinal blood vessels under both approaches provided an accuracy of just greater than 0.6, demonstrating that the segmented vessels do not add much utility to the deep learning-based analysis. The study’s findings show that the original-colored images are more significant in diagnosing retinopathy than the extracted retinal blood vessels.

Crop failure is defined as crop production that is significantly lower than anticipated, resulting from plants that are harmed, diseased, destroyed, or influenced by climatic circumstances. With the rise in global food security concern, the earliest detection of crop diseases has proven to be pivotal in agriculture industries to address the needs of the global food crisis and on-farm data protection, which can be met with a privacy-preserving deep learning model. However, deep learning seems to be a largely complex black box to interpret, necessitating a prerequisite for the groundwork of the model’s interpretability. Considering this, the aim of this study was to follow up on the establishment of a robust deep learning custom model named CropsDisNet, evaluated on a large-scale dataset named “New Bangladeshi Crop Disease Dataset (corn, potato and wheat)”, which contains a total of 8946 images. The integration of a differential privacy algorithm into our CropsDisNet model could establish the benefits of automated crop disease classification without compromising on-farm data privacy by reducing training data leakage. To classify corn, potato, and wheat leaf diseases, we used three representative CNN models for image classification (VGG16, Inception Resnet V2, Inception V3) along with our custom model, and the classification accuracy for these three different crops varied from 92.09% to 98.29%. In addition, demonstration of the model’s interpretability gave us insight into our model’s decision making and classification results, which can allow farmers to understand and take appropriate precautions in the event of early widespread harvest failure and food crises.

Breast cancer is a comprehensively dreadful disease that primarily affects women worldwide. In a lifetime, one out of every eight women is diagnosed with breast cancer. The probability of patient survival rate increases if it is properly diagnosed at the benign stage. However, when a cancer cell metastasizes out from its original development site, it enters into a secondary stage with increasing aggression, infecting other organs via the bloodstream and lymph system. At this stage, the cancer cells become life-threatening, and the survival rate of patients falls drastically. So it is evident that early diagnosis is essential to minimize the extent of the metastasis. Traditional diagnostics procedures by a pathologist are time-consuming and subject to human error. In this study, a custom-designed CNN network is proposed to distinguish metastasis cells from non-metastasis cells from histopathological breast cancer images. This proposed model has obtained an impressive training accuracy of 94.98% and a validation accuracy of 94.08%. The achieved average area under the receiver operating characteristic curve (AVC), Fr-score, recall, and precision of the CNN model are respectively-0.985, 0.94, 0.94, and 0.94. The experimental studies demonstrate the proposed model's ability to distinguish metastasis cells with high accuracy.