An India soyabean dataset for identification and classification of diseases using computer-vision algorithms.

Intelligent agriculture heavily relies on the science of agricultural disease image recognition. India is also responsible for large production of French beans, accounting for 37.25% of total production. In India from south region of Maharashtra state this crop is cultivated thrice in year. Soyabean plant is planted between the months of June through July, during the months of October and September during the rabi season, as well as in February. In the Maharashtrian regions of Pune, Satara, Ahmednagar, Solapur, and Nashik, among others, Soyabean plant is a common crop. In Maharashtra, Soyabean plant is grown over an area of around 31,050 hectares. This research presents a dataset of leaves from soyabean plants that are both insect-damaged and healthy. Images were taken over the course of fewer than two to three seasons on several farms. There are 3363 photos altogether in the seven folders that make up the dataset. Six categories comprise the dataset: I) Healthy plants II) Vein Necrosis III) Dry leaf IV) Septoria brown spot V) Root images VI) Bacterial leaf blight. This study's goal is to give academics and students accessibility to our dataset so they may use it for their studies and to build machine learning models.

Stochastic Dilated Residual Ghost Model for Breast Cancer Detection.

One of the most contentious issues in modern medicine is how to effectively standardise breast cancer screening. Deep learning models are already saving lives in the medical field due to their capacity to distinguish between benign and malignant tumours. Histopathology imaging poses difficulties due to the possibility of large colour variations caused by the staining technique and the biopsy material used to make the image; this problem leads to inaccurate breast cancer diagnoses. Our primary focus in this assessment is on the four main research concerns listed in the following: Overfitting and colour divergence must be rectified before moving on to other aspects of breast cancer categorisation. To overcome this issue, strain normalisation is utilised, and adding extra components is used to cope with overfitting; both techniques yielded positive results. The multiscale stochastic and dilation unit was then created to extract and enhance fine-grained characteristics such as edges, contours, and colour accuracy. To achieve this, the image is scaled to various different levels. The last challenge is to overcome the stochastic dilated residual ghost model's unreliability when used to recognise very tiny objects. The stochastic pooling block in this model makes effective use of downsampling to simplify the process without compromising the capacity to retrieve deep information. This upgrade was done as part of a bigger endeavour to eliminate unneeded or redundant components. In this case, we use convolution and identity mapping to create and maintain accurate mappings of the object's inherent characteristics. Upsampling is frequently used in conjunction with stochastic pooling to reduce feature dimensionality. The results of the experiments show that the suggested method is better than some of the current methods, with a network performance measurement area under the curve of 96.15 and percentages of 98.50 and 97.36.

Glaucoma Detection and Classification Using Improved U-Net Deep Learning Model.

Glaucoma is prominent in a variety of nations, with the United States and Europe being two of the most famous. Glaucoma now affects around 78 million people throughout the world (2020). By the year 2040, it is expected that there will be 111.8 million cases of glaucoma worldwide. In countries that are still building enough healthcare infrastructure to cope with glaucoma, the ailment is misdiagnosed nine times out of ten. To aid in the early diagnosis of glaucoma, the creation of a detection system is necessary. In this work, the researchers propose using a technology known as deep learning to identify and predict glaucoma before symptoms appear. The glaucoma dataset is used in this deep learning algorithm that has been proposed for analyzing glaucoma images. To get the required results when using deep learning principles for the job of segmenting the optic cup, pretrained transfer learning models are integrated with the U-Net architecture. For feature extraction, the DenseNet-201 deep convolution neural network (DCNN) is used. The DCNN approach is used to determine whether a person has glaucoma. The fundamental goal of this line of research is to recognize glaucoma in retinal fundus images, which will aid in assessing whether a patient has the condition. Because glaucoma can affect the model in both positive and negative ways, the model's outcome might be either positive or negative. Accuracy, precision, recall, specificity, the F-measure, and the F-score are some of the metrics used in the model evaluation process. An extra comparison study is performed as part of the process of establishing whether the suggested model is accurate. The findings are compared to convolution neural network classification methods based on deep learning. When used for training, the suggested model has an accuracy of 98.82 percent and an accuracy of 96.90 percent when used for testing. All assessments show that the new paradigm that has been proposed is more successful than the one that is currently in use.

Breast Cancer Detection on Histopathological Images Using a Composite Dilated Backbone Network.

Breast cancer is a lethal illness that has a high mortality rate. In treatment, the accuracy of diagnosis is crucial. Machine learning and deep learning may be beneficial to doctors. The proposed backbone network is critical for the present performance of CNN-based detectors. Integrating dilated convolution, ResNet, and Alexnet increases detection performance. The composite dilated backbone network (CDBN) is an innovative method for integrating many identical backbones into a single robust backbone. Hence, CDBN uses the lead backbone feature maps to identify objects. It feeds high-level output features from previous backbones into the next backbone in a stepwise way. We show that most contemporary detectors can easily include CDBN to improve performance achieved mAP improvements ranging from 1.5 to 3.0 percent on the breast cancer histopathological image classification (BreakHis) dataset. Experiments have also shown that instance segmentation may be improved. In the BreakHis dataset, CDBN enhances the baseline detector cascade mask R-CNN (mAP = 53.3). The proposed CDBN detector does not need pretraining. It creates high-level traits by combining low-level elements. This network is made up of several identical backbones that are linked together. The composite dilated backbone considers the linked backbones CDBN.