A study on deep feature extraction to detect and classify Acute Lymphoblastic Leukemia (ALL)

Acute lymphoblastic leukaemia (ALL) is a blood malignancy that mainly affects adults and children. This study looks into the use of deep learning, specifically Convolutional Neural Networks (CNNs), for the detection and classification of ALL. Conventional techniques for ALL diagnosis, such bone marrow biopsy, are costly and prone to mistakes made by hand. By utilising automated technologies, the research seeks to improve diagnostic accuracy. The research uses a variety of pre-trained CNN models, such as InceptionV3, ResNet101, VGG19, DenseNet121, MobileNetV2, and DenseNet121, to extract characteristics from pictures of blood smears. ANOVA, Recursive Feature Elimination (RFE), Random Forest, Lasso, and Principal Component Analysis (PCA) are a few of the selection approaches used to find the most relevant features after feature extraction. Following that, machine learning methods like Na\"ive Bayes, Random Forest, Support Vector Machine (SVM), and K-Nearest Neighbours (KNN) are used to classify these features. With an 87% accuracy rate, the ResNet101 model produced the best results, closely followed by DenseNet121 and VGG19. According to the study, CNN-based models have the potential to decrease the need for medical specialists by increasing the speed and accuracy of ALL diagnosis. To improve model performance, the study also recommends expanding and diversifying datasets and investigating more sophisticated designs such as transformers. This study highlights how well automated deep learning systems do medical diagnosis.

A Semantic Segmentation Approach on Sweet Orange Leaf Diseases Detection Utilizing YOLO

This research introduces an advanced method for diagnosing diseases in sweet orange leaves by utilising advanced artificial intelligence models like YOLOv8 . Due to their significance as a vital agricultural product, sweet oranges encounter significant threats from a variety of diseases that harmfully affect both their yield and quality. Conventional methods for disease detection primarily depend on manual inspection which is ineffective and frequently leads to errors, resulting in delayed treatment and increased financial losses. In response to this challenge, the research utilized YOLOv8 , harnessing their proficiencies in detecting objects and analyzing images. YOLOv8 is recognized for its rapid and precise performance, while VIT is acknowledged for its detailed feature extraction abilities. Impressively, during both the training and validation stages, YOLOv8 exhibited a perfect accuracy of 80.4%, while VIT achieved an accuracy of 99.12%, showcasing their potential to transform disease detection in agriculture. The study comprehensively examined the practical challenges related to the implementation of AI technologies in agriculture, encompassing the computational demands and user accessibility, and offering viable solutions for broader usage. Moreover, it underscores the environmental considerations, particularly the potential for reduced pesticide usage, thereby promoting sustainable farming and environmental conservation. These findings provide encouraging insights into the application of AI in agriculture, suggesting a transition towards more effective, sustainable, and technologically advanced farming methods. This research not only highlights the efficacy of YOLOv8 within a specific agricultural domain but also lays the foundation for further studies that encompass a broader application in crop management and sustainable agricultural practices.

A study on Deep Convolutional Neural Networks, Transfer Learning and Ensemble Model for Breast Cancer Detection

In deep learning, transfer learning and ensemble models have shown promise in improving computer-aided disease diagnosis. However, applying the transfer learning and ensemble model is still relatively limited. Moreover, the ensemble model's development is ad-hoc, overlooks redundant layers, and suffers from imbalanced datasets and inadequate augmentation. Lastly, significant Deep Convolutional Neural Networks (D-CNNs) have been introduced to detect and classify breast cancer. Still, very few comparative studies were conducted to investigate the accuracy and efficiency of existing CNN architectures. Realising the gaps, this study compares the performance of D-CNN, which includes the original CNN, transfer learning, and an ensemble model, in detecting breast cancer. The comparison study of this paper consists of comparison using six CNN-based deep learning architectures (SE-ResNet152, MobileNetV2, VGG19, ResNet18, InceptionV3, and DenseNet-121), a transfer learning, and an ensemble model on breast cancer detection. Among the comparison of these models, the ensemble model provides the highest detection and classification accuracy of 99.94% for breast cancer detection and classification. However, this study also provides a negative result in the case of transfer learning, as the transfer learning did not increase the accuracy of the original SE-ResNet152, MobileNetV2, VGG19, ResNet18, InceptionV3, and DenseNet-121 model. The high accuracy in detecting and categorising breast cancer detection using CNN suggests that the CNN model is promising in breast cancer disease detection. This research is significant in biomedical engineering, computer-aided disease diagnosis, and ML-based disease detection.

A Comprehensive Literature Review on Sweet Orange Leaf Diseases

Sweet orange leaf diseases are significant to agricultural productivity. Leaf diseases impact fruit quality in the citrus industry. The apparition of machine learning makes the development of disease finder. Early detection and diagnosis are necessary for leaf management. Sweet orange leaf disease-predicting automated systems have already been developed using different image-processing techniques. This comprehensive literature review is systematically based on leaf disease and machine learning methodologies applied to the detection of damaged leaves via image classification. The benefits and limitations of different machine learning models, including Vision Transformer (ViT), Neural Network (CNN), CNN with SoftMax and RBF SVM, Hybrid CNN-SVM, HLB-ConvMLP, EfficientNet-b0, YOLOv5, YOLOv7, Convolutional, Deep CNN. These machine learning models tested on various datasets and detected the disease. This comprehensive review study related to leaf disease compares the performance of the models; those models' accuracy, precision, recall, etc., were used in the subsisting studies

Machine Learning-Based Jamun Leaf Disease Detection: A Comprehensive Review

Jamun leaf diseases pose a significant threat to agricultural productivity, negatively impacting both yield and quality in the jamun industry. The advent of machine learning has opened up new avenues for tackling these diseases effectively. Early detection and diagnosis are essential for successful crop management. While no automated systems have yet been developed specifically for jamun leaf disease detection, various automated systems have been implemented for similar types of disease detection using image processing techniques. This paper presents a comprehensive review of machine learning methodologies employed for diagnosing plant leaf diseases through image classification, which can be adapted for jamun leaf disease detection. It meticulously assesses the strengths and limitations of various Vision Transformer models, including Transfer learning model and vision transformer (TLMViT), SLViT, SE-ViT, IterationViT, Tiny-LeViT, IEM-ViT, GreenViT, and PMViT. Additionally, the paper reviews models such as Dense Convolutional Network (DenseNet), Residual Neural Network (ResNet)-50V2, EfficientNet, Ensemble model, Convolutional Neural Network (CNN), and Locally Reversible Transformer. These machine-learning models have been evaluated on various datasets, demonstrating their real-world applicability. This review not only sheds light on current advancements in the field but also provides valuable insights for future research directions in machine learning-based jamun leaf disease detection and classification.

Machine Learning-Based Tea Leaf Disease Detection: A Comprehensive Review

Tea leaf diseases are a major challenge to agricultural productivity, with far-reaching implications for yield and quality in the tea industry. The rise of machine learning has enabled the development of innovative approaches to combat these diseases. Early detection and diagnosis are crucial for effective crop management. For predicting tea leaf disease, several automated systems have already been developed using different image processing techniques. This paper delivers a systematic review of the literature on machine learning methodologies applied to diagnose tea leaf disease via image classification. It thoroughly evaluates the strengths and constraints of various Vision Transformer models, including Inception Convolutional Vision Transformer (ICVT), GreenViT, PlantXViT, PlantViT, MSCVT, Transfer Learning Model & Vision Transformer (TLMViT), IterationViT, IEM-ViT. Moreover, this paper also reviews models like Dense Convolutional Network (DenseNet), Residual Neural Network (ResNet)-50V2, YOLOv5, YOLOv7, Convolutional Neural Network (CNN), Deep CNN, Non-dominated Sorting Genetic Algorithm (NSGA-II), MobileNetv2, and Lesion-Aware Visual Transformer. These machine-learning models have been tested on various datasets, demonstrating their real-world applicability. This review study not only highlights current progress in the field but also provides valuable insights for future research directions in the machine learning-based detection and classification of tea leaf diseases.

A comprehensive review on Plant Leaf Disease detection using Deep learning

Leaf disease is a common fatal disease for plants. Early diagnosis and detection is necessary in order to improve the prognosis of leaf diseases affecting plant. For predicting leaf disease, several automated systems have already been developed using different plant pathology imaging modalities. This paper provides a systematic review of the literature on leaf disease-based models for the diagnosis of various plant leaf diseases via deep learning. The advantages and limitations of different deep learning models including Vision Transformer (ViT), Deep convolutional neural network (DCNN), Convolutional neural network (CNN), Residual Skip Network-based Super-Resolution for Leaf Disease Detection (RSNSR-LDD), Disease Detection Network (DDN), and YOLO (You only look once) are described in this review. The review also shows that the studies related to leaf disease detection applied different deep learning models to a number of publicly available datasets. For comparing the performance of the models, different metrics such as accuracy, precision, recall, etc. were used in the existing studies.