In order to support the creation of reliable machine learning models for anomaly detection, this project focuses on preprocessing, enhancing, and organizing a medical imaging dataset. There are two classifications in the dataset: normal and abnormal, along with extra noise fluctuations. In order to improve the photographs' quality, undesirable artifacts, including visible medical equipment at the edges, were eliminated using central cropping. Adjusting the brightness and contrast was one of the additional preprocessing processes. Normalization was then performed to normalize the data. To make classification jobs easier, the dataset was methodically handled by combining several image subsets into two primary categories: normal and pathological. To provide a strong training set that adapts well to real-world situations, sophisticated picture preprocessing techniques were used, such as contrast enhancement and real-time augmentation (including rotations, zooms, and brightness modifications). To guarantee efficient model evaluation, the data was subsequently divided into training and testing subsets. In order to create precise and effective machine learning models for medical anomaly detection, high-quality input data is ensured via this thorough approach. Because of the project pipeline's flexible and scalable design, it can be easily integrated with bigger clinical decision-support systems.

Cervical cancer remains a major worldwide health issue, with early identification and risk assessment playing critical roles in effective preventive interventions. This paper presents the Cervix-AID-Net model for cervical precancer risk classification. The study designs and evaluates the proposed Cervix-AID-Net model based on patients colposcopy images. The model comprises a Convolutional Block Attention Module (CBAM) and convolutional layers that extract interpretable and representative features of colposcopic images to distinguish high-risk and low-risk cervical precancer. In addition, the proposed Cervix-AID-Net model integrates four explainable techniques, namely gradient class activation maps, Local Interpretable Model-agnostic Explanations, CartoonX, and pixel rate distortion explanation based on output feature maps and input features. The evaluation using holdout and ten-fold cross-validation techniques yielded a classification accuracy of 99.33\% and 99.81\%. The analysis revealed that CartoonX provides meticulous explanations for the decision of the Cervix-AID-Net model due to its ability to provide the relevant piece-wise smooth part of the image. The effect of Gaussian noise and blur on the input shows that the performance remains unchanged up to Gaussian noise of 3\% and blur of 10\%, while the performance reduces thereafter. A comparison study of the proposed model's performance compared to other deep learning approaches highlights the Cervix-AID-Net model's potential as a supplemental tool for increasing the effectiveness of cervical precancer risk assessment. The proposed method, which incorporates the CBAM and explainable artificial integration, has the potential to influence cervical cancer prevention and early detection, improving patient outcomes and lowering the worldwide burden of this preventable disease.

Cervical cancer stands as a predominant cause of female mortality, underscoring the need for regular screenings to enable early diagnosis and preemptive treatment of pre-cancerous conditions. The transformation zone in the cervix, where cellular differentiation occurs, plays a critical role in the detection of abnormalities. Colposcopy has emerged as a pivotal tool in cervical cancer prevention since it provides a meticulous examination of cervical abnormalities. However, challenges in visual evaluation necessitate the development of Computer Aided Diagnosis (CAD) systems. We propose a novel CAD system that combines the strengths of various deep-learning descriptors (ResNet50, ResNet101, and ResNet152) with appropriate feature normalization (min-max) as well as feature reduction technique (LDA). The combination of different descriptors ensures that all the features (low-level like edges and colour, high-level like shape and texture) are captured, feature normalization prevents biased learning, and feature reduction avoids overfitting. We do experiments on the IARC dataset provided by WHO. The dataset is initially segmented and balanced. Our approach achieves exceptional performance in the range of 97%-100% for both the normal-abnormal and the type classification. A competitive approach for type classification on the same dataset achieved 81%-91% performance.