A Review on the Emergence of Intelligent Oncology by Sensor-Guided Drug Delivery

Kazi Kutubuddin Sayyad Liyakat — Sat, 19 Sep 2026 00:00:00 +0000

For decades, the fight against cancer has been a brutal war of attrition. Chemotherapy, medicine’s necessary evil, operates like a scorched-earth campaign—highly effective, yet devastatingly indiscriminate, destroying both the invasive enemy cells and the healthy host tissue. Conventional cancer chemotherapy often suffers from limitations such as non-specific biodistribution, systemic toxicity, and inadequate drug concentrations at tumor sites, leading to suboptimal therapeutic outcomes and severe side effects. This study presents the transformative potential of integrating advanced sensor technology with drug delivery systems to address these critical oncology challenges. By enabling real-time monitoring and responsive drug release, sensor-enabled platforms promise to revolutionize cancer treatment through enhanced precision, personalization, and efficacy. This study explores innovative approaches where miniaturized biosensors (e.g., electrochemical, optical, pH, temperature, or biomarker-responsive) are incorporated into smart drug carriers (e.g., nanoparticles, hydrogels, implantable microdevices). These intelligent systems are designed to sense specific tumor microenvironmental cues (e.g., abnormal pH, hypoxia, enzyme overexpression) or patient physiological parameters, subsequently triggering the controlled and localized release of anticancer agents. This feedback-controlled mechanism ensures targeted delivery, minimizes off-target accumulation, and allows for on-demand dosing adjustments, thereby maximizing therapeutic effects while significantly reducing systemic toxicity. The integration of sensor technology ushers in an era of truly personalized cancer therapy, offering hope for improved patient quality of life and more effective disease management.

Automated Handwritten Text Analysis and Recognition Using Neural Networks

Vaishnavi S. Bhandigare, Sanchita S. Mudrale, Pradnya P. Bhandigare, Sonal Ayare — Fri, 25 Sep 2026 00:00:00 +0000

Handwritten text recognition (HTR) is a difficult problem in Optical Character Recognition (OCR) systems, owing to the extreme variability of handwriting styles, degradation of image quality, and diverse character appearances. The present paper proposes a comprehensive framework based on deep learning techniques, which incorporates Convolutional Neural Networks (CNNs), Bidirectional Long Short-Term Memory (BiLSTM) models, and Connectionist Temporal Classification (CTC) for effective handwritten text recognition. The hybrid model utilizes CNNs to learn spatial features from handwritten text images, BiLSTMs to learn sequential relationships between characters, and CTC to learn sequences directly from images without requiring explicit character segmentation. The model incorporates various preprocessing techniques to improve its performance, including image normalization, noise reduction, and data augmentation, to achieve robustness against diverse handwriting styles. The performance of the model has been evaluated on benchmark datasets, achieving state-of-the-art performance with a character error rate of 2.95% and a word error rate of less than 7%. The model achieves an accuracy of 94% during validation, with efficient computation to suit practical scenarios. The present study contributes to the advancement of automated document digitization, accessibility for the visually impaired, efficient data processing in banking, healthcare, and education, among other areas.

International Journal of Image Processing and Smart Sensors

A Review on the Emergence of Intelligent Oncology by Sensor-Guided Drug Delivery

Automated Handwritten Text Analysis and Recognition Using Neural Networks