Potatoes play a vital role as a staple crop worldwide, making a significant contribution to global food security. However, the susceptibility of potato plants to various leaf diseases poses a threat to crop yield and quality. Detecting these diseases accurately and at an early stage is crucial for the effective management and protection of crops. Recent advancements in Convolutional Neural Networks (CNNs) have demonstrated potential in image categorization applications. Therefore, the goal of this work is to investigate the potential of CNNs in detecting potato leaf diseases. As neural networks have become part of agriculture, numerous researchers have worked on improving the early detection of potato blight using different machine and deep learning methods. However, there are persistent problems related to accuracy and the time it takes for these methods to work. In response to these challenges, we tailored a convolutional neural network (CNN) to enhance accuracy while reducing the trainable parameters, computational time and information loss. To conduct this research, we compiled a diverse dataset consisting of images of potato leaves. The dataset encompassed both healthy leaves and leaves infected with common diseases such as late blight and early blight. We took great care in curating and preprocessing the dataset to ensure its quality and consistency. Our focus was to develop a specialized CNN architecture tailored specifically for disease detection. To improve the performance of the network, we employed techniques like data augmentation and transfer learning during the training phase. The experimental outcomes demonstrate the efficacy of our proposed customized CNN model in accurately identifying and classifying potato leaf diseases. Our model's overall accuracy was an astounding 99.22%, surpassing the performance of existing methods by a significant margin. Furthermore, we evaluated precision, recall, and F1-score to evaluate the model's effectiveness on individual disease classes. To give an additional understanding of the model's behavior and its capacity to distinguish between various disease types, we utilized visualization techniques such as confusion matrices and sample output images. The results of this study have implications for managing potato diseases by offering an automated and reliable solution for early detection and diagnosis. Future research directions may include expanding the dataset, exploring different CNN architectures, and investigating the generalizability of the model across different potato varieties and growing conditions.

Ultrasound is mostly used for diagnosis to deal with the specific abnormality in human body. To observe the internal organs including liver, kidneys, pancreas, thyroid gland, ovaries etc. ultrasound can be used. In diagnostic applications, 2 to 18 MHz frequencies are used. The sound wave explorations occurred through soft tissue and fluids. It bounces back as echoes from denser surfaces and creates an image. While producing ultrasound images from echo signal speckle noise is induced in a multiplicative way. Thus, speckle becomes the key challenge for ultrasound imaging. Several speckle reducing linear, non-linear and anisotropic diffusion-based methods are implemented to preserve the sharp edges of ultrasound images. Those methods contain lake of smoothing and edge preservation. However, this research proposed a combined method of adaptive filter (wiener) and anisotropic diffusion (modified Perona Malik) for speckle reduction of 2D ultrasound images by retain the important anatomical features. A comparison of all the existing methods studied based on the simulated experiment. To test the methods liver, kidney, heart and pancreas noise free images are used. Then, speckle noise is manually added with distinguished variance in between 0.02 and 0.20. Quality metrics are used to test the performance and show the improvements of the proposed method. About 71.79% structure similarity (SSIM), 66.72% root mean square error (RMSE), 56.93% signal to noise ratio (SNR), and 62.30% computational time are improved on average compared with the other methods.