Leukemia is a fatal disease of white blood cells which affects the blood and bone marrow in human body. We deployed deep convolutional neural network for automated detection of acute lymphoblastic leukemia and classification of its subtypes into 4 classes, that is, L1, L2, L3, and Normal which were mostly neglected in previous literature. In contrary to the training from scratch, we deployed pretrained AlexNet which was fine-tuned on our data set. Last layers of the pretrained network were replaced with new layers which can classify the input images into 4 classes. To reduce overtraining, data augmentation technique was used. We also compared the data sets with different color models to check the performance over different color images. For acute lymphoblastic leukemia detection, we achieved a sensitivity of 100%, specificity of 98.11%, and accuracy of 99.50%; and for acute lymphoblastic leukemia subtype classification the sensitivity was 96.74%, specificity was 99.03%, and accuracy was 96.06%. Unlike the standard methods, our proposed method was able to achieve high accuracy without any need of microscopic image segmentation.

Leukaemia is a form of blood cancer which affects the white blood cells and damages the bone marrow. Usually complete blood count (CBC) and bone marrow aspiration are used to diagnose the acute lymphoblastic leukaemia. It can be a fatal disease if not diagnosed at the earlier stage. In practice, manual microscopic evaluation of stained sample slide is used for diagnosis of leukaemia. But manual diagnostic methods are time-consuming, less accurate, and prone to errors due to various human factors like stress, fatigue, and so forth. Therefore, different automated systems have been proposed to wrestle the glitches in the manual diagnostic methods. In recent past, some computer-aided leukaemia diagnosis methods are presented. These automated systems are fast, reliable, and accurate as compared to manual diagnosis methods. This paper presents review of computer-aided diagnosis systems regarding their methodologies that include enhancement, segmentation, feature extraction, classification, and accuracy.

Marine Debris is human-created waste dumped into the sea or ocean. It pollutes the aquatic environment and hence very dangerous for ocean species. Removal of marine debris from ocean is necessary to eliminate pollution and to secure aquatic life. A robust and automatic system is essential that detects unnecessary litter of plastic and other garbage at real-time. In this study, we have proposed deep learning based architecture for the detection and classification of marine debris. Histogram Equalization technique combined with Median Filter is used to enhance the contrast of images and to remove noise. Experiments are performed on challenging Forward Looking Sonar Image (FLS) Marine Debris Dataset. This dataset includes ten different types of Debris. The proposed system not only detect the Debris, but also classify it into ten classes. To overcome the challenge of data scarcity, Faster-RCNN with transfer learning of ResNet-50 architecture is used. Faster-RCNN is one of the popular object detection architecture that uses Regional Proposal Network (RPN) and detector at the same time. The proposed methodology significantly improves the state-of-the-art results. Result assessment of our proposed technique achieved recall of (96%) and Mean Overlap bounding boxes of (3.78). Visual and qualitative assessment of proposed methodology shows the effectiveness of presented technique.

Alzheimer’s disease is a common neurological disorder and mental disability that causes memory loss and cognitive decline, presenting a major challenge to public health due to its impact on millions of individuals worldwide. It is crucial to diagnose and treat Alzheimer’s in a timely manner to improve the quality of life of both patients and caregivers. In the recent past, machine learning techniques have showed potential in detecting Alzheimer’s disease by examining neuroimaging data, especially Magnetic Resonance Imaging (MRI). This research proposes an attention-based mechanism that employs the vision transformer approach to detect Alzheimer’s using MRI images. The presented technique applies preprocessing to the MRI images and forwards them to a vision transformer network for classification. This network is trained on the publicly available Kaggle dataset, and it illustrated impressive results with an accuracy of 99.06%, precision of 99.06%, recall of 99.14%, and F1-score of 99.1%. Furthermore, a comparative study is also conducted to evaluate the performance of the proposed method against various state-of-the-art techniques on diverse datasets. The proposed method demonstrated superior performance, outperforming other published methods when applied to the Kaggle dataset.

Detecting and classifying objects from satellite images are crucial for many applications, ranging from marine monitoring to land planning, ecology to warfare, etc. Spatial and temporal information-rich satellite images are exploited in a variety of manners to solve many real-world remote sensing problems. Satellite image classification has many associated challenges. These challenges include data availability, the quality of data, the quantity of data, and data distribution. These challenges make the analysis of satellite images more challenging. A convolutional neural network architecture with a scaling method is proposed for the classification of satellite images. The scaling method can evenly scale all dimensions of depth, width, and resolution using a compound coefficient. It can be used as a preliminary task in urban planning, satellite surveillance, monitoring, etc. It can also be helpful in geo-information and maritime monitoring systems. The proposed methodology is based on an end-to-end, scalable satellite image interpretation. It uses spatial information from satellite images to categorize these into four categories. The proposed method gives encouraging and promising results on a challenging dataset with a high inter-class similarity and intra-class variation. The proposed method shows 99.64% accuracy on the RSI-CB256 dataset.

Failure of facial recognition and authentication system may lead to several unlawful activities. The current facial recognition systems are vulnerable to different biometric attacks. This research focuses on morphing attack detection. This research proposes a robust detection mechanism that can deal with variation in age, illumination, eye and head gears. A deep learning based feature extractor along with a classifier is adopted. Additionally, image enhancement and feature combination are proposed to augment the detection results. A versatile dataset is also developed that contains Morph-2 and Morph-3 images, created by sophisticated tools with manual intervention. Morph-3 images can give more realistic appearance and hence difficult to detect. Moreover, Morph-3 images are not considered in the literature before. Professional morphing software depicts more realistic morph attack scenario as compared to the morphs generated in the previous work from free programs and code scripts. Eight face databases are used for creation of morphs to encompass the variation. These databases are Celebrity2000, Extended Yale, FEI, FGNET, GT-DB, MULTI-PIE, FERET and FRLL. Results are investigated using multiple experimental setups and it is concluded that the proposed methodology gives promising results.