In the world of civilized medical scientific progression, cancer has become a very serious threat for the natural survival of human beings where breast cancer stays to be the second most dangerous type. Mostly women are embracing very pathetic death because of the delayed detection of the cancer cell in the certain period of their life. Machine Learning mechanism can definitely help at the stage of medical imaging which can escalate the diagnosis of the cancer cells at a very early age of its biological formation and development. We focused upon the deep learning approach to classify the normal and abnormal breast according to the medical imaging from the MIAS dataset of Mammograms and Pixel Intensity. The Convolution Neural Network (CNN) alongside ResNet, AmoebaNet and EfficientNet have been used for the detection with 330 mammograms in which 194 images are normal and 136 are having the identification of abnormal breasts. The accuracy of the entire experimental results was carrying the torch of potential legacy of deep learning in the medical imaging arena. The research is ongoing for the further development and optimization of CNN, AmoebaNet-C and EfficientNet architecture for the Pixel Intensity with higher accuracy, proper segmentation and masking. Source code of this research is available here: https://github.com/ac005sheekar/Breast-Cancer-Detection-with-Pixel-Intensity/

Robot Vision is the technique of enabling robots to process visual data from the environment by utilizing a combination of camera hardware and computer algorithms. Advanced deep neural networks have significantly played a vital role in indulging robots to make more sense out of complex visual data at different circumstances, especially in object detection and continuous tracking. In this research, we initiated a unique and cutting-edge backbone neural network for the conventional YOLO algorithm which we named as SBHK-Net. The network boosted up the performance of the existing YOLO algorithm drastically which manifests a strong potential of improving tracking and recognition accuracies of other conventional algorithms in the robot vision industry as well. It has the greatest accuracy 59.2% AP among all known real-time object detectors with 30 FPS or above on GPU RTX3060, and it outperforms all other known object detectors in the range of 5 FPS to 160 FPS. We used YOLOv7 as our reference point for the core research. The transformer-based detector SWINL Cascade-Mask R-CNN (9.2 FPS A100, 53.9% AP) and the convolutional detector ConvNeXt-XL Cascade-Mask R-CNN (8.6 FPS A100, 55.2% AP) are both outperformed by the SBHK-Net core object detector (56 FPS RTX3060, 56.4% AP) in terms of speed and accuracy, respectively. In terms of speed and accuracy, it surpasses a number of other object detectors, including DINO-5scale-R50, ViT-Adapter-B, Scaled-YOLOv4, YOLOv5, DETR, Deformable DETR, and YOLOR and YoLOX. The source code of this research is available at https://github.com/ac005sheekar/SBHKNet .

Undoubtedly breast cancer identifies itself as one of the most widespread and terrifying cancers across the globe. Millions of women are getting affected each year from it. Breast cancer remains the major one for being the reason of largest number of demise of women. In the recent time of research, Medical Image Computing and Processing has been playing a significant role for detecting and classifying breast cancers from ultrasound images and mammograms, along with the celestial touch of deep neural networks. In this research, we focused mostly on our rigorous implementations and iterative result analysis of different cutting-edge modified versions of EfficientNet architectures namely EfficientNet-V1 (b0-b7) and EfficientNet-V2 (b0-b3) with ultrasound image, named as CEIMVEN. We utilized transfer learning approach here for using the pre-trained models of EfficientNet versions. We activated the hyper-parameter tuning procedures, added fully connected layers, discarded the unprecedented outliers and recorded the accuracy results from our custom modified EfficientNet architectures. Our deep learning model training approach was related to both identifying the cancer affected areas with region of interest (ROI) techniques and multiple classifications (benign, malignant and normal). The approximate testing accuracies we got from the modified versions of EfficientNet-V1 (b0- 99.15%, b1- 98.58%, b2- 98.43%, b3- 98.01%, b4- 98.86%, b5- 97.72%, b6- 97.72%, b7- 98.72%) and EfficientNet-V2 (b0- 99.29%, b1- 99.01%, b2- 98.72%, b3- 99.43%) are showing very bright future and strong potentials of deep learning approach for the successful detection and classification of breast cancers from the ultrasound images at a very early stage. The code for this research is available here: https://github.com/ac005sheekar/CEIMVEN-Breast.