Mango is a highly significant fruit crop that thrives in a variety of agro-ecologies around the world. Mangoes are rich in vitamins and minerals. However, its yield is currently severely constrained due to disease and pest infestations. Thus, in order to improve mango fruit quality and productivity, illnesses and insect pests must be detected early on. In this study, we conceived and constructed a mango leaf disease detection mechanism utilizing an ensemble convolutional neural network approach. Healthy and diseased mango leaf images were manually obtained from main producing locations in Amhara Region for Merawi fruit and vegetable research identification. To improve the datasets, several pre-processing procedures (such as image resizing, noise reduction, and image augmentation) were used. To improve classification performance and meet the study's purpose, various segmentation approaches such as k means and Mask R-CNN were applied. Furthermore, following pre-processing and segmentation, features of mango leaf images were retrieved using CNN to obtain important features. The classification model was then constructed using fully-connected layer classifiers on the retrieved features of mango leaf images. The ensemble proposed GoogLeNet and VGG16 based CNN model in the study encompasses various operations, including dataset collection, image preprocessing, noise removal, segmentation, data augmentation, feature extraction, and classification. Upon testing, the model demonstrated impressive performance with 99.87 % training classification accuracy, 99.72 % validation accuracy, and 99.21 % testing accuracy. This indicates the effectiveness of the ensemble approach in achieving high accuracy in image classification tasks.

Degenerative diseases of the lumbar spine, such as spondylolisthesis, disc degeneration, and lumbar spinal stenosis, are major contributors to global disability. Accurate classification of lumbar diseases is crucial for effective medical diagnosis. This paper introduces an innovative methodology for lumbar disease classification, addressing the limitations of traditional convolutional neural networks (CNNs). We propose a novel approach, InVGG, which combines involutional neural networks with the VGG architecture. Unlike traditional CNNs, InVGG utilizes involution kernels that are location-specific and channel-agnostic, enhancing its adaptability to varied visual patterns in medical images. Our study focuses on a four-class lumbar disease classification problem using sagittal T2 MRI images. The evaluation of InVGG is compared with traditional CNNs (VGG model) and machine learning algorithms, demonstrating superior performance in terms of accuracy, precision, recall, and AUC ROC values. InVGG achieves an impressive 96% accuracy on the testing set and 99% on the training set, showcasing its potential for accurate spinal lumbar disease classification. The reduced parameter count of InVGG compared to CNNs (VGG) makes it more resource-efficient, especially in scenarios with limited computational resources and datasets. The promising results position InVGG as a valuable tool for precise lumbar disease classification, with implications for improving patient care in resource-constrained scenarios.

Breast cancer is the most prevalent form of tumor in women, and this is the leading cause of mortality in women. Accurately detecting and classifying breast cancer is crucial for effective treatment and diagnosis preparation. Internet of Things (IoT) wearable devices (smart bra) are considered one of the best methods for early detection and, thereby, reducing breast cancer mortality. This study proposes a unique approach for breast cancer classification that combines involutional neural networks (InvNets) with wearable devices to reduce the parameter-intensive nature of convolutional neural networks. Specifically, the involution kernel differs from the convolution kernel because it is location-specific and channel-agnostic. This location-specific operation enhances the network's ability to acquire detailed elements in medical images by adapting to diverse visual patterns across spatial regions. In that regard, magnetic resonance imaging (MRI) has become an important modality for breast cancer detection. This work investigates the use of IoT devices such as wearables and sensors to collect patient health data and monitor changes in breast tissue. If symptoms are present, an MRI scan is performed for a reliable diagnosis of breast cancer utilizing deep learning (InvNets). Our findings show that InvNets achieves 100% training accuracy, 98.5% validation accuracy, and 98.6% testing accuracy after data augmentation, indicating its potential for breast cancer classification. InvNets are highly successful for medical image processing, particularly in circumstances with limited computer resources, as seen by enhanced accuracy and reduced parameter count. As a result, the findings show that InvNets provided consistent and reliable features for breast cancer detection and faster diagnosis.